Small Molecule & Simulation
Kinase selectivity, non-standard force-field parameters, and purchasable focused libraries converge on a finite experimental decision.
Explore Small Molecule & Simulation →19 CAT solutions
MULTIMODAL R&D DECISION SYSTEMSFrom candidate space
From candidate space
to the next experiment.
Convert molecular, omics, imaging, and materials data into calibrated shortlists, constrained experimental portfolios, and traceable delivery packages.
INPUTStructureSequenceImageOmicsMaterialsEvidence
CoreEXPERIMENT
CoreEXPERIMENT
7platform domains
50product lines
158CAT solutions
217offline pages
HOT TOPIC · PUBLIC-DATA CASE
How do 388 somatic mutations become a balanced 34-neoantigen mRNA payload?
HLA-I/HLA-II presentation, clonality, tumor expression, immunogenicity, manufacturability, and junction risk are evaluated separately before a constrained portfolio is assembled.
388 mutations→26,118 peptide–HLA predictions→34 neoantigens→1 mRNA construct
Open the case study ↗PLATFORM CAPABILITIES
Seven domains, one experimental decision framework.
Biologics Design
Structural, evolutionary, safety, immunogenicity, and manufacturability evidence is combined into experiment-ready biologic designs.
Explore Biologics Design →19 CAT solutionsGenomics & Editing
Version-controlled variant, transcript, HLA, and editing evidence is compiled into validation queues, guide sets, and therapeutic payloads.
Explore Genomics & Editing →24 CAT solutionsSingle-cell & Tissue
Reference mapping, tissue safety, translational deconvolution, model audit, and immune-repertoire analysis are delivered with explicit validation gates.
Explore Single-cell & Tissue →26 CAT solutionsNucleic Acid Therapeutics
siRNA, ASO, mRNA, circRNA, and aptamer programs use modality-specific efficacy, safety, manufacturability, and assay constraints.
Explore Nucleic Acid Therapeutics →22 CAT solutionsBioimaging & Spatial Biology
IHC, multiplex pathology, H&E, spatial transcriptomics, MRI/CT, response imaging, and multimodal registration produce auditable regions, phenotypes, and validation panels.
Explore Bioimaging & Spatial Biology →24 CAT solutionsAI-guided Materials R&D
Machine-learning potentials, electronic structure, adsorption, interfaces, crystals, and electrochemistry narrow compositions and structures to experimentally tractable sets.
Explore AI-guided Materials R&D →24 CAT solutionsAI Product Atlas
Research solutions organized by experimental decision.
Every CAT entry has a defined target, candidate funnel, evidence framework, execution plan, and downstream experimental endpoint.
7platform domains
50product lines
158CAT solutions
217offline pages
MOLDomain overview →
Small Molecule & Simulation
Advance selective compounds and simulation-ready chemical assets.
KIN
KINSEL-AIKinase Selectivity Design
PAR
ParamReady-AISimulation-ready Force-field Parameters
MOL-PAR-001Covalent-warhead Parameter PlatformProject-defined target and comparator setMOL-PAR-ACR-001Acrylamide–Cysteine Parameter PackageDedicated route under MOL-PAR-001MOL-PAR-CYA-001Cyanoacrylamide Reversible-covalent Parameter PackageDedicated route under MOL-PAR-001MOL-PAR-SUF-001Sulfonyl-fluoride Residue-selective Parameter PackageDedicated route under MOL-PAR-001MOL-PAR-0022'-OMe, 2'-F, 2'-MOE, LNA, PS — Simulation-ready Force-field Parameters2'-OMe, 2'-F, 2'-MOE, LNA, PSMOL-PAR-003LNPSM-102, ALC-0315, MC3, C12-200MOL-PAR-004p Ser/p Thr/p Tyr, acK, meK — Simulation-ready Force-field Parametersp Ser/p Thr/p Tyr, acK, meKMOL-PAR-005Zn, Mg, Fe-heme, Cu, Mn — Simulation-ready Force-field ParametersZn, Mg, Fe-heme, Cu, MnMOL-PAR-006ADC linker-payloadvc-PAB, GGFG, MMAE, Dxd, DM1FOC
FocusLibrary-AITarget-focused Purchasable Libraries
SEP
SafetyEpitope-AITarget–Epitope Safety Design
PEV
ProteinEvo-AIProtein Variant Library Design
BIO-PEV-001Industrial Enzyme Activity–Stability Libraryω-/KRED and related targetsBIO-PEV-002Antibody Affinity–Expression LibraryIgG/VHH CDR and sitesBIO-PEV-003PETase Thermostability Variant LibraryPETase/FAST-PETaseBIO-PEV-004Cas9 Specificity Variant LibrarySp Cas9/Cas12aBIO-PEV-005Therapeutic Enzyme Stability–Immunogenicity LibraryProject-defined target and comparator setDEV
Developability-AIAntibody Developability Triage
VAR
VariantRank-AIVUS Triage and Validation Queue
PGX
PGxMap-AIPharmacogenomic Interpretation
SPL
SpliceMap-AISplicing Variant Mechanism Design
EDT
EditGuide-AIGenome-editing Design Sets
FUS
FusionMap-AIFusion Breakpoint Interpretation
NEO
Neoantigen-AINeoantigen Portfolio and Construct Design
ATL
CellAtlas-AIDisease Reference Atlases
SAF
SafetyMap-AINormal-tissue Target Safety Maps
MOD
ModelMatch-AICell-line and Organoid Selection
DEC
ClinicalDeconv-AISingle-cell to Clinical Cohort Translation
AUD
ModelAudit-AISingle-cell Foundation-model Audit
NADomain overview →
Nucleic Acid Therapeutics
Select sequences, chemistries, and construct architectures.
SIR
siRNASelect-AIsiRNA Portfolio Design
ASO
ASOSelect-AIASO Gapmer and Splice Design
MRN
mRNAConstruct-AImRNA Construct Design
CIR
circRNAConstruct-AICircular RNA Construct Design
PAT
IHCQuant-AIIHC and Multiplex Pathology Quantification
IMG-IHC-001Breast Cancer CD8 IHC Spatial QuantificationCD8+ cells × OncologyIMG-IHC-002PD-L1 TPS/CPS Computation and ReviewPD-L1 IHC × Oncologycells/cellsIMG-IHC-003HER2 Heterogeneity and Membrane-completeness AnalysisHER2 IHC × regionsIMG-IHC-004Multiplex IHC Immune-neighborhood AnalysisCD8/CD4/FOXP3/CD68/PanCKSPX
SpatialMap-AISpatial Transcriptomic States and Niches
IMG-SPX-001Colorectal Cancer Immune–Tumor Boundary AtlasCRC tumor/adjacent Visium + scRNA referenceIMG-SPX-002Breast Cancer Immune-exclusion Nichebreast tumor spatial RNA + H&E + IHCIMG-SPX-003Liver Fibrosis Spatial Zonationfibrotic liver zonation + stellate/macrophage statesIMG-SPX-004Neuroinflammatory Cell Neighborhoodsbrain spatial transcriptomics + glial referenceHST
HistoFeature-AIH&E Morphology and Weakly Supervised Models
IMG-HST-001H&E Immune-hot/Cold Phenotypebreast/NSCLC H&E + immune labelsIMG-HST-002Research-use Colorectal MSI PrescreenCRC H&E + MSI statusIMG-HST-003Digital Morphology Score for Liver Fibrosisliver biopsy H&E/trichromeIMG-HST-004Tumor Necrosis and Treatment-effect Quantificationresection H&E pre/post therapyRAD
RadioPath-AIImaging-to-Microenvironment Prediction
RSP
ResponseImage-AILongitudinal Imaging and Treatment Response
IMG-RSP-001Breast Cancer Neoadjuvant DCE-MRI Early Responsebaseline/early-treatment DCE-MRI × pCR/RCBIMG-RSP-002ICI Lesion-level Response and Pseudoprogressionserial CT × lesion response × immune outcomesIMG-RSP-003ADC Treatment-response Imaging Heterogeneityserial CT/MRI × lesion-level responseIMG-RSP-004Drug Longitudinal Tissue Imagingpaired biopsy/SHG/MRIREG
SpatialBridge-AICross-modal Registration and Label Transfer
IMG-REG-001H&E–IHC– Trimodal Registrationserial sections/same-slide multimodal tissueIMG-REG-002MRI– slides Spatial Bridgingresection specimen × ex vivo/in vivo MRIIMG-REG-003cells Reference MappingIMC/CODEX × scRNA referenceIMG-REG-004Drug regions Cross-modal Validationspatial RNA × phospho-IHC × morphologyCAS
CatScreen-AICatalyst Adsorption Screening
VOL
VolcanoMap-AIVolcano Maps and Reaction Barriers
SAC
SAC-AISingle-atom and Doped Catalysts
BAV
BatVolt-AIElectrode Intercalation Voltage
ELY
Electrolyte-AIElectrolyte Stability Window and Solvation
SIO
SolidION-AISolid-electrolyte Ion Migration
RDX
RedoxFinger-AIRedox Potentials and PCET
SAR
SensorArray-AIRecognition Materials and Array Deconvolution
INF
Interferent-AIInterferent Separation Design
MPR
MaterialParam-AIMaterials and Interface Force-field Parameters
TOR
TorsionQM-AITorsion and Conformer Reference Data
COR
CorrosionGuard-AICorrosion-inhibitor Adsorption Screening
IBF
InterfaceBind-AIInterface Binding and Charge Transfer
COA
Coating-AICoatings, Passivation, and Antifouling
FMO
FMO-AIFrontier-orbital and Reactivity Fingerprints
BND
Binding-AIHost–Guest and Cluster Binding
CHG
ChargeXfer-AIDifference Density and Charge Transfer
MOF
MOFCapture-AIPorous-material Adsorption Screening
POL
Polymorph-AIMolecular-crystal Polymorph Ranking
STB
Stability-AIFormation Energy and Phase Stability
AI Product Atlas
Small Molecule & Simulation
Kinase selectivity, non-standard force-field parameters, and purchasable focused libraries converge on a finite experimental decision.
PRODUCT LINES
3 decision systems in this domain.
KINSEL-AI
Kinase Selectivity Design
Turns ·/·Selectivity evidence into a capacity-matched experimental package.
Open product line →ParamReady-AI
Simulation-ready Force-field Parameters
Turns · evidence into a capacity-matched experimental package.
Open product line →FocusLibrary-AI
Target-focused Purchasable Libraries
Turns ·Focused Library evidence into a capacity-matched experimental package.
Open product line →AI Product Atlas
Biologics Design
Structural, evolutionary, safety, immunogenicity, and manufacturability evidence is combined into experiment-ready biologic designs.
PRODUCT LINES
4 decision systems in this domain.
SafetyEpitope-AI
Target–Epitope Safety Design
Turns ·/cells· evidence into a capacity-matched experimental package.
Open product line →ProteinEvo-AI
Protein Variant Library Design
Turns · evidence into a capacity-matched experimental package.
Open product line →Developability-AI
Antibody Developability Triage
Turns · evidence into a capacity-matched experimental package.
Open product line →DeImmunize-AI
De-immunized Construct Design
Turns · evidence into a capacity-matched experimental package.
Open product line →AI Product Atlas
Genomics & Editing
Version-controlled variant, transcript, HLA, and editing evidence is compiled into validation queues, guide sets, and therapeutic payloads.
PRODUCT LINES
6 decision systems in this domain.
VariantRank-AI
VUS Triage and Validation Queue
Turns · evidence into a capacity-matched experimental package.
Open product line →PGxMap-AI
Pharmacogenomic Interpretation
Turns · evidence into a capacity-matched experimental package.
Open product line →SpliceMap-AI
Splicing Variant Mechanism Design
Turns · evidence into a capacity-matched experimental package.
Open product line →EditGuide-AI
Genome-editing Design Sets
Turns · evidence into a capacity-matched experimental package.
Open product line →FusionMap-AI
Fusion Breakpoint Interpretation
Turns ·structures evidence into a capacity-matched experimental package.
Open product line →Neoantigen-AI
Neoantigen Portfolio and Construct Design
Turns · evidence into a capacity-matched experimental package.
Open product line →AI Product Atlas
Single-cell & Tissue
Reference mapping, tissue safety, translational deconvolution, model audit, and immune-repertoire analysis are delivered with explicit validation gates.
PRODUCT LINES
6 decision systems in this domain.
CellAtlas-AI
Disease Reference Atlases
Turns cells·Reference Atlas evidence into a capacity-matched experimental package.
Open product line →SafetyMap-AI
Normal-tissue Target Safety Maps
Turns cells· evidence into a capacity-matched experimental package.
Open product line →ModelMatch-AI
Cell-line and Organoid Selection
Turns cells· evidence into a capacity-matched experimental package.
Open product line →ClinicalDeconv-AI
Single-cell to Clinical Cohort Translation
Turns cells· evidence into a capacity-matched experimental package.
Open product line →ModelAudit-AI
Single-cell Foundation-model Audit
Turns cells·Model Audit evidence into a capacity-matched experimental package.
Open product line →Repertoire-AI
TCR/BCR Clone Selection
Turns cells· evidence into a capacity-matched experimental package.
Open product line →AI Product Atlas
Nucleic Acid Therapeutics
siRNA, ASO, mRNA, circRNA, and aptamer programs use modality-specific efficacy, safety, manufacturability, and assay constraints.
PRODUCT LINES
5 decision systems in this domain.
siRNASelect-AI
siRNA Portfolio Design
Turns · evidence into a capacity-matched experimental package.
Open product line →ASOSelect-AI
ASO Gapmer and Splice Design
Turns · evidence into a capacity-matched experimental package.
Open product line →mRNAConstruct-AI
mRNA Construct Design
Turns · evidence into a capacity-matched experimental package.
Open product line →circRNAConstruct-AI
Circular RNA Construct Design
Turns · evidence into a capacity-matched experimental package.
Open product line →AptamerSeed-AI
Aptamer Seed-pool Design
Turns · evidence into a capacity-matched experimental package.
Open product line →AI Product Atlas
Bioimaging & Spatial Biology
IHC, multiplex pathology, H&E, spatial transcriptomics, MRI/CT, response imaging, and multimodal registration produce auditable regions, phenotypes, and validation panels.
PRODUCT LINES
6 decision systems in this domain.
IHCQuant-AI
IHC and Multiplex Pathology Quantification
Turns ·cellsand evidence into a capacity-matched experimental package.
Open product line →SpatialMap-AI
Spatial Transcriptomic States and Niches
Turns ·statesandneighborhoods evidence into a capacity-matched experimental package.
Open product line →HistoFeature-AI
H&E Morphology and Weakly Supervised Models
Turns · evidence into a capacity-matched experimental package.
Open product line →RadioPath-AI
Imaging-to-Microenvironment Prediction
Turns · evidence into a capacity-matched experimental package.
Open product line →ResponseImage-AI
Longitudinal Imaging and Treatment Response
Turns · evidence into a capacity-matched experimental package.
Open product line →SpatialBridge-AI
Cross-modal Registration and Label Transfer
Turns ·Spatial Bridging evidence into a capacity-matched experimental package.
Open product line →AI Product Atlas
AI-guided Materials R&D
Machine-learning potentials, electronic structure, adsorption, interfaces, crystals, and electrochemistry narrow compositions and structures to experimentally tractable sets.
PRODUCT LINES
20 decision systems in this domain.
CatScreen-AI
Catalyst Adsorption Screening
Turns Catalysisand Catalysis evidence into a capacity-matched experimental package.
Open product line →VolcanoMap-AI
Volcano Maps and Reaction Barriers
Turns Catalysisand Catalysis evidence into a capacity-matched experimental package.
Open product line →SAC-AI
Single-atom and Doped Catalysts
Turns Catalysisand Catalysis evidence into a capacity-matched experimental package.
Open product line →BatVolt-AI
Electrode Intercalation Voltage
Turns Materialsand evidence into a capacity-matched experimental package.
Open product line →Electrolyte-AI
Electrolyte Stability Window and Solvation
Turns Materialsand evidence into a capacity-matched experimental package.
Open product line →SolidION-AI
Solid-electrolyte Ion Migration
Turns Materialsand evidence into a capacity-matched experimental package.
Open product line →RedoxFinger-AI
Redox Potentials and PCET
Turns and evidence into a capacity-matched experimental package.
Open product line →SensorArray-AI
Recognition Materials and Array Deconvolution
Turns and evidence into a capacity-matched experimental package.
Open product line →Interferent-AI
Interferent Separation Design
Turns and evidence into a capacity-matched experimental package.
Open product line →MaterialParam-AI
Materials and Interface Force-field Parameters
Turns ai-guided materials r&d evidence into a capacity-matched experimental package.
Open product line →TorsionQM-AI
Torsion and Conformer Reference Data
Turns ai-guided materials r&d evidence into a capacity-matched experimental package.
Open product line →CorrosionGuard-AI
Corrosion-inhibitor Adsorption Screening
Turns surfaces· · evidence into a capacity-matched experimental package.
Open product line →InterfaceBind-AI
Interface Binding and Charge Transfer
Turns surfaces· · evidence into a capacity-matched experimental package.
Open product line →Coating-AI
Coatings, Passivation, and Antifouling
Turns surfaces· · evidence into a capacity-matched experimental package.
Open product line →FMO-AI
Frontier-orbital and Reactivity Fingerprints
Turns and evidence into a capacity-matched experimental package.
Open product line →Binding-AI
Host–Guest and Cluster Binding
Turns and evidence into a capacity-matched experimental package.
Open product line →ChargeXfer-AI
Difference Density and Charge Transfer
Turns and evidence into a capacity-matched experimental package.
Open product line →MOFCapture-AI
Porous-material Adsorption Screening
Turns ·MOF· evidence into a capacity-matched experimental package.
Open product line →Polymorph-AI
Molecular-crystal Polymorph Ranking
Turns ·MOF· evidence into a capacity-matched experimental package.
Open product line →Stability-AI
Formation Energy and Phase Stability
Turns ·MOF· evidence into a capacity-matched experimental package.
Open product line →AI Product Atlas
MOL-KIN · PRODUCT LINE
KINSEL-AI
Kinase Selectivity Design
Designed to answer one actionable R&D question: which candidates, controls, and validation experiments should advance together?
DECISION WORKFLOW
Five evidence-preserving steps.
01
Lock the input, endpoint, controls, and exclusions
02
Generate a traceable candidate space
03
Calibrate orthogonal evidence and uncertainty
04
Select a diverse set under experimental constraints
05
Deliver assay-ready files and a data-return schema
CAT SOLUTIONS
5 defined solution packages.
MOL-KIN-001CDK9 Selectivity Package
CDK9 vs. CDK2/7/12
1,284 compounds → 8 advancement candidates → 6 counter-screen targetsOpen solution →MOL-KIN-002JAK1 Selectivity Package
JAK1 vs. JAK2/JAK3/TYK2
960 compounds → 9 advancement candidates → 5 counter-screen targetsOpen solution →MOL-KIN-003BTK Selectivity Package
BTK vs. TEC/BMX/ITK
742 compounds → 7 advancement candidates → 5 counter-screen targetsOpen solution →MOL-KIN-004PI3Kα Selectivity Package
PI3Kα vs. PI3Kβ/δ/γ
1,116 compounds → 10 advancement candidates → 6 counter-screen targetsOpen solution →MOL-KIN-005CDK4/6 Selectivity Package
CDK4/6 vs. CDK2/9
884 compounds → 8 advancement candidates → 4 counter-screen targetsOpen solution →AI Product Atlas
MOL-KIN-001 · Illustrative planning example
CDK9 Selectivity Package
CDK9 vs. CDK2/7/12
Small Molecule & Simulation research and development
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Input and assay framing
Define CDK9 vs. CDK2/7/12, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · State and conformer generation
Generate a traceable candidate space appropriate to kinase selectivity design.
candidate-space file and provenance03 · Physics and evidence calibration
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Diverse constrained selection
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Experimental package
Select a diverse, capacity-matched set for Small Molecule & Simulation research and development.
ranked experimental package06 · Data return and model update
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Next-round decision
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
MOL-KIN-002 · Illustrative planning example
JAK1 Selectivity Package
JAK1 vs. JAK2/JAK3/TYK2
and
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Input and assay framing
Define JAK1 vs. JAK2/JAK3/TYK2, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · State and conformer generation
Generate a traceable candidate space appropriate to kinase selectivity design.
candidate-space file and provenance03 · Physics and evidence calibration
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Diverse constrained selection
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Experimental package
Select a diverse, capacity-matched set for and.
ranked experimental package06 · Data return and model update
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Next-round decision
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
MOL-KIN-003 · Illustrative planning example
BTK Selectivity Package
BTK vs. TEC/BMX/ITK
B cells
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Input and assay framing
Define BTK vs. TEC/BMX/ITK, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · State and conformer generation
Generate a traceable candidate space appropriate to kinase selectivity design.
candidate-space file and provenance03 · Physics and evidence calibration
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Diverse constrained selection
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Experimental package
Select a diverse, capacity-matched set for B cells.
ranked experimental package06 · Data return and model update
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Next-round decision
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
MOL-KIN-004 · Illustrative planning example
PI3Kα Selectivity Package
PI3Kα vs. PI3Kβ/δ/γ
Oncologyand
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Input and assay framing
Define PI3Kα vs. PI3Kβ/δ/γ, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · State and conformer generation
Generate a traceable candidate space appropriate to kinase selectivity design.
candidate-space file and provenance03 · Physics and evidence calibration
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Diverse constrained selection
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Experimental package
Select a diverse, capacity-matched set for Oncologyand.
ranked experimental package06 · Data return and model update
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Next-round decision
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
MOL-KIN-005 · Illustrative planning example
CDK4/6 Selectivity Package
CDK4/6 vs. CDK2/9
cells
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Input and assay framing
Define CDK4/6 vs. CDK2/9, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · State and conformer generation
Generate a traceable candidate space appropriate to kinase selectivity design.
candidate-space file and provenance03 · Physics and evidence calibration
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Diverse constrained selection
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Experimental package
Select a diverse, capacity-matched set for cells.
ranked experimental package06 · Data return and model update
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Next-round decision
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
MOL-PAR · PRODUCT LINE
ParamReady-AI
Simulation-ready Force-field Parameters
Designed to answer one actionable R&D question: which candidates, controls, and validation experiments should advance together?
DECISION WORKFLOW
Five evidence-preserving steps.
01
Lock the input, endpoint, controls, and exclusions
02
Generate a traceable candidate space
03
Calibrate orthogonal evidence and uncertainty
04
Select a diverse set under experimental constraints
05
Deliver assay-ready files and a data-return schema
CAT SOLUTIONS
6 defined solution packages.
MOL-PAR-001Covalent-warhead Parameter Platform
Project-defined target and comparator set
3 → 3 →Open solution →MOL-PAR-0022'-OMe, 2'-F, 2'-MOE, LNA, PS — Simulation-ready Force-field Parameters
2'-OMe, 2'-F, 2'-MOE, LNA, PS
18 → → 4 engine formatsOpen solution →MOL-PAR-003LNP
SM-102, ALC-0315, MC3, C12-200
12 → pH states → all-atom/coarse-grained versionsOpen solution →MOL-PAR-004p Ser/p Thr/p Tyr, acK, meK — Simulation-ready Force-field Parameters
p Ser/p Thr/p Tyr, acK, meK
24 → protonation calibration → protein example systemOpen solution →MOL-PAR-005Zn, Mg, Fe-heme, Cu, Mn — Simulation-ready Force-field Parameters
Zn, Mg, Fe-heme, Cu, Mn
8 →/→ validationOpen solution →MOL-PAR-006ADC linker-payload
vc-PAB, GGFG, MMAE, Dxd, DM1
20 linker/payload → →Open solution →AI Product Atlas
MOL-PAR-001 · Illustrative planning example
Covalent-warhead Parameter Platform
Project-defined target and comparator set
Small Molecule & Simulation research and development
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Input and assay framing
Define Project-defined target and comparator set, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · State and conformer generation
Generate a traceable candidate space appropriate to simulation-ready force-field parameters.
candidate-space file and provenance03 · Physics and evidence calibration
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Diverse constrained selection
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Experimental package
Select a diverse, capacity-matched set for Small Molecule & Simulation research and development.
ranked experimental package06 · Data return and model update
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Next-round decision
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
MOL-PAR-ACR-001 · Illustrative planning example
Acrylamide–Cysteine Parameter Package
Acrylamide–Cys Michael
Small Molecule & Simulation research and development
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Input and assay framing
Define Acrylamide–Cys Michael, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · State and conformer generation
Generate a traceable candidate space appropriate to simulation-ready force-field parameters.
candidate-space file and provenance03 · Physics and evidence calibration
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Diverse constrained selection
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Experimental package
Select a diverse, capacity-matched set for Small Molecule & Simulation research and development.
ranked experimental package06 · Data return and model update
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Next-round decision
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
MOL-PAR-CYA-001 · Illustrative planning example
Cyanoacrylamide Reversible-covalent Parameter Package
Cyanoacrylamide–Cys states
Small Molecule & Simulation research and development
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Input and assay framing
Define Cyanoacrylamide–Cys states, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · State and conformer generation
Generate a traceable candidate space appropriate to simulation-ready force-field parameters.
candidate-space file and provenance03 · Physics and evidence calibration
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Diverse constrained selection
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Experimental package
Select a diverse, capacity-matched set for Small Molecule & Simulation research and development.
ranked experimental package06 · Data return and model update
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Next-round decision
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
MOL-PAR-SUF-001 · Illustrative planning example
Sulfonyl-fluoride Residue-selective Parameter Package
Sulfonyl fluoride and Lys/Tyr/Ser/Cys
SuFEx and
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Input and assay framing
Define Sulfonyl fluoride and Lys/Tyr/Ser/Cys, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · State and conformer generation
Generate a traceable candidate space appropriate to simulation-ready force-field parameters.
candidate-space file and provenance03 · Physics and evidence calibration
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Diverse constrained selection
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Experimental package
Select a diverse, capacity-matched set for SuFEx and.
ranked experimental package06 · Data return and model update
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Next-round decision
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
MOL-PAR-002 · Illustrative planning example
2'-OMe, 2'-F, 2'-MOE, LNA, PS — Simulation-ready Force-field Parameters
2'-OMe, 2'-F, 2'-MOE, LNA, PS
siRNA/ASO/mRNA
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Input and assay framing
Define 2'-OMe, 2'-F, 2'-MOE, LNA, PS, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · State and conformer generation
Generate a traceable candidate space appropriate to simulation-ready force-field parameters.
candidate-space file and provenance03 · Physics and evidence calibration
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Diverse constrained selection
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Experimental package
Select a diverse, capacity-matched set for siRNA/ASO/mRNA.
ranked experimental package06 · Data return and model update
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Next-round decision
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
MOL-PAR-003 · Illustrative planning example
LNP
SM-102, ALC-0315, MC3, C12-200
Deliveryand Vaccine
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Input and assay framing
Define SM-102, ALC-0315, MC3, C12-200, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · State and conformer generation
Generate a traceable candidate space appropriate to simulation-ready force-field parameters.
candidate-space file and provenance03 · Physics and evidence calibration
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Diverse constrained selection
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Experimental package
Select a diverse, capacity-matched set for Deliveryand Vaccine.
ranked experimental package06 · Data return and model update
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Next-round decision
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
MOL-PAR-004 · Illustrative planning example
p Ser/p Thr/p Tyr, acK, meK — Simulation-ready Force-field Parameters
p Ser/p Thr/p Tyr, acK, meK
and
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Input and assay framing
Define p Ser/p Thr/p Tyr, acK, meK, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · State and conformer generation
Generate a traceable candidate space appropriate to simulation-ready force-field parameters.
candidate-space file and provenance03 · Physics and evidence calibration
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Diverse constrained selection
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Experimental package
Select a diverse, capacity-matched set for and.
ranked experimental package06 · Data return and model update
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Next-round decision
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
MOL-PAR-005 · Illustrative planning example
Zn, Mg, Fe-heme, Cu, Mn — Simulation-ready Force-field Parameters
Zn, Mg, Fe-heme, Cu, Mn
and CYP
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Input and assay framing
Define Zn, Mg, Fe-heme, Cu, Mn, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · State and conformer generation
Generate a traceable candidate space appropriate to simulation-ready force-field parameters.
candidate-space file and provenance03 · Physics and evidence calibration
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Diverse constrained selection
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Experimental package
Select a diverse, capacity-matched set for and CYP.
ranked experimental package06 · Data return and model update
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Next-round decision
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
MOL-PAR-006 · Illustrative planning example
ADC linker-payload
vc-PAB, GGFG, MMAE, Dxd, DM1
ADC and Bioconjugation
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Input and assay framing
Define vc-PAB, GGFG, MMAE, Dxd, DM1, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · State and conformer generation
Generate a traceable candidate space appropriate to simulation-ready force-field parameters.
candidate-space file and provenance03 · Physics and evidence calibration
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Diverse constrained selection
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Experimental package
Select a diverse, capacity-matched set for ADC and Bioconjugation.
ranked experimental package06 · Data return and model update
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Next-round decision
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
MOL-FOC · PRODUCT LINE
FocusLibrary-AI
Target-focused Purchasable Libraries
Designed to answer one actionable R&D question: which candidates, controls, and validation experiments should advance together?
DECISION WORKFLOW
Five evidence-preserving steps.
01
Lock the input, endpoint, controls, and exclusions
02
Generate a traceable candidate space
03
Calibrate orthogonal evidence and uncertainty
04
Select a diverse set under experimental constraints
05
Deliver assay-ready files and a data-return schema
CAT SOLUTIONS
5 defined solution packages.
MOL-FOC-001KRAS G12D Focused Library
KRAS G12D Switch-II pocket
50,000 candidates → 1,800 designs → 120Open solution →MOL-FOC-002WRN Focused Library
WRN helicase/ATPase
32,000 candidates → 1,500 designs → 96Open solution →MOL-FOC-003PRMT5–MTA Focused Library
PRMT5 MTA-cooperative pocket
28,000 candidates → 1,400 designs → 82Open solution →MOL-FOC-004TEAD Focused Library
TEAD1/4
20,000 candidates → 1,200 designs → 74Open solution →MOL-FOC-005SHP2 Focused Library
SHP2 allosteric pocket
36,000 candidates → 1,600 designs → 105Open solution →AI Product Atlas
MOL-FOC-001 · Illustrative planning example
KRAS G12D Focused Library
KRAS G12D Switch-II pocket
KRAS
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Input and assay framing
Define KRAS G12D Switch-II pocket, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · State and conformer generation
Generate a traceable candidate space appropriate to target-focused purchasable libraries.
candidate-space file and provenance03 · Physics and evidence calibration
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Diverse constrained selection
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Experimental package
Select a diverse, capacity-matched set for KRAS.
ranked experimental package06 · Data return and model update
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Next-round decision
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
MOL-FOC-002 · Illustrative planning example
WRN Focused Library
WRN helicase/ATPase
Small Molecule & Simulation research and development
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Input and assay framing
Define WRN helicase/ATPase, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · State and conformer generation
Generate a traceable candidate space appropriate to target-focused purchasable libraries.
candidate-space file and provenance03 · Physics and evidence calibration
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Diverse constrained selection
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Experimental package
Select a diverse, capacity-matched set for Small Molecule & Simulation research and development.
ranked experimental package06 · Data return and model update
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Next-round decision
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
MOL-FOC-003 · Illustrative planning example
PRMT5–MTA Focused Library
PRMT5 MTA-cooperative pocket
Small Molecule & Simulation research and development
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Input and assay framing
Define PRMT5 MTA-cooperative pocket, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · State and conformer generation
Generate a traceable candidate space appropriate to target-focused purchasable libraries.
candidate-space file and provenance03 · Physics and evidence calibration
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Diverse constrained selection
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Experimental package
Select a diverse, capacity-matched set for Small Molecule & Simulation research and development.
ranked experimental package06 · Data return and model update
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Next-round decision
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
MOL-FOC-004 · Illustrative planning example
TEAD Focused Library
TEAD1/4
Hippo
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Input and assay framing
Define TEAD1/4, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · State and conformer generation
Generate a traceable candidate space appropriate to target-focused purchasable libraries.
candidate-space file and provenance03 · Physics and evidence calibration
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Diverse constrained selection
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Experimental package
Select a diverse, capacity-matched set for Hippo.
ranked experimental package06 · Data return and model update
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Next-round decision
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
MOL-FOC-005 · Illustrative planning example
SHP2 Focused Library
SHP2 allosteric pocket
Small Molecule & Simulation research and development
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Input and assay framing
Define SHP2 allosteric pocket, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · State and conformer generation
Generate a traceable candidate space appropriate to target-focused purchasable libraries.
candidate-space file and provenance03 · Physics and evidence calibration
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Diverse constrained selection
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Experimental package
Select a diverse, capacity-matched set for Small Molecule & Simulation research and development.
ranked experimental package06 · Data return and model update
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Next-round decision
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
BIO-SEP · PRODUCT LINE
SafetyEpitope-AI
Target–Epitope Safety Design
Designed to answer one actionable R&D question: which candidates, controls, and validation experiments should advance together?
DECISION WORKFLOW
Five evidence-preserving steps.
01
Lock the input, endpoint, controls, and exclusions
02
Generate a traceable candidate space
03
Calibrate orthogonal evidence and uncertainty
04
Select a diverse set under experimental constraints
05
Deliver assay-ready files and a data-return schema
CAT SOLUTIONS
6 defined solution packages.
BIO-SEP-001TROP2 Epitope Safety Design
TROP2/TACSTD2
58 surface patches → 4 epitopes → 4/validationOpen solution →BIO-SEP-002CLDN18.2 Epitope Safety Design
CLDN18.2 loops
34 surface patches → 4 epitopes → 3/validationOpen solution →BIO-SEP-003B7-H3 Differentiated Epitope Design
4Ig-B7-H3/CD276
64 surface patches → 5 epitopes → 3 competition assaysOpen solution →BIO-SEP-004GPC3 Epitope Safety Design
GPC3
49 surface patches → 4 epitopes → 4 validationOpen solution →BIO-SEP-005DLL3 Epitope Safety Design
DLL3
41 surface patches → 3 epitopes → 3 Neuro validationOpen solution →BIO-SEP-006GPRC5D Epitope Safety Design
GPRC5D
38 surface patches → 4 epitopes → 4/NeurovalidationOpen solution →AI Product Atlas
BIO-SEP-001 · Illustrative planning example
TROP2 Epitope Safety Design
TROP2/TACSTD2
ADC/TCE
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Target product profile
Define TROP2/TACSTD2, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Structure and sequence preparation
Generate a traceable candidate space appropriate to target–epitope safety design.
candidate-space file and provenance03 · Variant or epitope generation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Safety and developability calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Library or construct selection
Select a diverse, capacity-matched set for ADC/TCE.
ranked experimental package06 · Experimental validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Closed-loop redesign
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
BIO-SEP-002 · Illustrative planning example
CLDN18.2 Epitope Safety Design
CLDN18.2 loops
CAR-T/ADC
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Target product profile
Define CLDN18.2 loops, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Structure and sequence preparation
Generate a traceable candidate space appropriate to target–epitope safety design.
candidate-space file and provenance03 · Variant or epitope generation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Safety and developability calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Library or construct selection
Select a diverse, capacity-matched set for CAR-T/ADC.
ranked experimental package06 · Experimental validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Closed-loop redesign
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
BIO-SEP-003 · Illustrative planning example
B7-H3 Differentiated Epitope Design
4Ig-B7-H3/CD276
ADC
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Target product profile
Define 4Ig-B7-H3/CD276, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Structure and sequence preparation
Generate a traceable candidate space appropriate to target–epitope safety design.
candidate-space file and provenance03 · Variant or epitope generation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Safety and developability calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Library or construct selection
Select a diverse, capacity-matched set for ADC.
ranked experimental package06 · Experimental validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Closed-loop redesign
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
BIO-SEP-004 · Illustrative planning example
GPC3 Epitope Safety Design
GPC3
Liver Cancer CAR-T/TCE
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Target product profile
Define GPC3, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Structure and sequence preparation
Generate a traceable candidate space appropriate to target–epitope safety design.
candidate-space file and provenance03 · Variant or epitope generation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Safety and developability calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Library or construct selection
Select a diverse, capacity-matched set for Liver Cancer CAR-T/TCE.
ranked experimental package06 · Experimental validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Closed-loop redesign
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
BIO-SEP-005 · Illustrative planning example
DLL3 Epitope Safety Design
DLL3
SCLC ADC/TCE
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Target product profile
Define DLL3, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Structure and sequence preparation
Generate a traceable candidate space appropriate to target–epitope safety design.
candidate-space file and provenance03 · Variant or epitope generation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Safety and developability calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Library or construct selection
Select a diverse, capacity-matched set for SCLC ADC/TCE.
ranked experimental package06 · Experimental validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Closed-loop redesign
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
BIO-SEP-006 · Illustrative planning example
GPRC5D Epitope Safety Design
GPRC5D
Biologics Design research and development
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Target product profile
Define GPRC5D, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Structure and sequence preparation
Generate a traceable candidate space appropriate to target–epitope safety design.
candidate-space file and provenance03 · Variant or epitope generation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Safety and developability calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Library or construct selection
Select a diverse, capacity-matched set for Biologics Design research and development.
ranked experimental package06 · Experimental validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Closed-loop redesign
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
BIO-PEV · PRODUCT LINE
ProteinEvo-AI
Protein Variant Library Design
Designed to answer one actionable R&D question: which candidates, controls, and validation experiments should advance together?
DECISION WORKFLOW
Five evidence-preserving steps.
01
Lock the input, endpoint, controls, and exclusions
02
Generate a traceable candidate space
03
Calibrate orthogonal evidence and uncertainty
04
Select a diverse set under experimental constraints
05
Deliver assay-ready files and a data-return schema
CAT SOLUTIONS
5 defined solution packages.
BIO-PEV-001Industrial Enzyme Activity–Stability Library
ω-/KRED and related targets
3,842 designs → 88 variants + 8 controls → 96 wellsOpen solution →BIO-PEV-002Antibody Affinity–Expression Library
IgG/VHH CDR and sites
2,460 designs → 80 variants + 16 controls → 96 wellsOpen solution →BIO-PEV-003PETase Thermostability Variant Library
PETase/FAST-PETase
5,120 designs → 88 variants + 8 controls → 96 wellsOpen solution →BIO-PEV-004Cas9 Specificity Variant Library
Sp Cas9/Cas12a
4,320 designs → 84 variants + 12 controls → 96 wellsOpen solution →BIO-PEV-005Therapeutic Enzyme Stability–Immunogenicity Library
Project-defined target and comparator set
2,980 designs → 72 variants + 24 controls → 96 wellsOpen solution →AI Product Atlas
BIO-PEV-001 · Illustrative planning example
Industrial Enzyme Activity–Stability Library
ω-/KRED and related targets
Biologics Design research and development
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Target product profile
Define ω-/KRED and related targets, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Structure and sequence preparation
Generate a traceable candidate space appropriate to protein variant library design.
candidate-space file and provenance03 · Variant or epitope generation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Safety and developability calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Library or construct selection
Select a diverse, capacity-matched set for Biologics Design research and development.
ranked experimental package06 · Experimental validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Closed-loop redesign
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
BIO-PEV-002 · Illustrative planning example
Antibody Affinity–Expression Library
IgG/VHH CDR and sites
Biologics Design research and development
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Target product profile
Define IgG/VHH CDR and sites, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Structure and sequence preparation
Generate a traceable candidate space appropriate to protein variant library design.
candidate-space file and provenance03 · Variant or epitope generation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Safety and developability calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Library or construct selection
Select a diverse, capacity-matched set for Biologics Design research and development.
ranked experimental package06 · Experimental validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Closed-loop redesign
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
BIO-PEV-003 · Illustrative planning example
PETase Thermostability Variant Library
PETase/FAST-PETase
Biologics Design research and development
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Target product profile
Define PETase/FAST-PETase, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Structure and sequence preparation
Generate a traceable candidate space appropriate to protein variant library design.
candidate-space file and provenance03 · Variant or epitope generation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Safety and developability calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Library or construct selection
Select a diverse, capacity-matched set for Biologics Design research and development.
ranked experimental package06 · Experimental validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Closed-loop redesign
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
BIO-PEV-004 · Illustrative planning example
Cas9 Specificity Variant Library
Sp Cas9/Cas12a
Biologics Design research and development
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Target product profile
Define Sp Cas9/Cas12a, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Structure and sequence preparation
Generate a traceable candidate space appropriate to protein variant library design.
candidate-space file and provenance03 · Variant or epitope generation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Safety and developability calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Library or construct selection
Select a diverse, capacity-matched set for Biologics Design research and development.
ranked experimental package06 · Experimental validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Closed-loop redesign
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
BIO-PEV-005 · Illustrative planning example
Therapeutic Enzyme Stability–Immunogenicity Library
Project-defined target and comparator set
Biologics Design research and development
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Target product profile
Define Project-defined target and comparator set, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Structure and sequence preparation
Generate a traceable candidate space appropriate to protein variant library design.
candidate-space file and provenance03 · Variant or epitope generation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Safety and developability calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Library or construct selection
Select a diverse, capacity-matched set for Biologics Design research and development.
ranked experimental package06 · Experimental validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Closed-loop redesign
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
BIO-DEV · PRODUCT LINE
Developability-AI
Antibody Developability Triage
Designed to answer one actionable R&D question: which candidates, controls, and validation experiments should advance together?
DECISION WORKFLOW
Five evidence-preserving steps.
01
Lock the input, endpoint, controls, and exclusions
02
Generate a traceable candidate space
03
Calibrate orthogonal evidence and uncertainty
04
Select a diverse set under experimental constraints
05
Deliver assay-ready files and a data-return schema
CAT SOLUTIONS
4 defined solution packages.
BIO-DEV-001IgG candidates Developability Triage
10–30 IgG candidates
24 candidates → 6 advancement candidates → 4 CMCOpen solution →BIO-DEV-002configurations Developability Triage
Cross Mab/KiH/IgG-sc Fv
16 configurations → 4 advancement candidates → 5 riskOpen solution →BIO-DEV-003VHH Developability Triage
VHH/nanobody
32 candidates → 8 advancement candidates → 4Open solution →BIO-DEV-004ADC
IgG + sitescandidates
18/sitesdesigns → 5 advancement candidates → DAR/Open solution →AI Product Atlas
BIO-DEV-001 · Illustrative planning example
IgG candidates Developability Triage
10–30 IgG candidates
Biologics Design research and development
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Target product profile
Define 10–30 IgG candidates, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Structure and sequence preparation
Generate a traceable candidate space appropriate to antibody developability triage.
candidate-space file and provenance03 · Variant or epitope generation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Safety and developability calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Library or construct selection
Select a diverse, capacity-matched set for Biologics Design research and development.
ranked experimental package06 · Experimental validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Closed-loop redesign
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
BIO-DEV-002 · Illustrative planning example
configurations Developability Triage
Cross Mab/KiH/IgG-sc Fv
Biologics Design research and development
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Target product profile
Define Cross Mab/KiH/IgG-sc Fv, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Structure and sequence preparation
Generate a traceable candidate space appropriate to antibody developability triage.
candidate-space file and provenance03 · Variant or epitope generation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Safety and developability calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Library or construct selection
Select a diverse, capacity-matched set for Biologics Design research and development.
ranked experimental package06 · Experimental validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Closed-loop redesign
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
BIO-DEV-003 · Illustrative planning example
VHH Developability Triage
VHH/nanobody
Biologics Design research and development
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Target product profile
Define VHH/nanobody, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Structure and sequence preparation
Generate a traceable candidate space appropriate to antibody developability triage.
candidate-space file and provenance03 · Variant or epitope generation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Safety and developability calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Library or construct selection
Select a diverse, capacity-matched set for Biologics Design research and development.
ranked experimental package06 · Experimental validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Closed-loop redesign
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
BIO-DEV-004 · Illustrative planning example
ADC
IgG + sitescandidates
ADC
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Target product profile
Define IgG + sitescandidates, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Structure and sequence preparation
Generate a traceable candidate space appropriate to antibody developability triage.
candidate-space file and provenance03 · Variant or epitope generation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Safety and developability calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Library or construct selection
Select a diverse, capacity-matched set for ADC.
ranked experimental package06 · Experimental validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Closed-loop redesign
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
BIO-DIM · PRODUCT LINE
DeImmunize-AI
De-immunized Construct Design
Designed to answer one actionable R&D question: which candidates, controls, and validation experiments should advance together?
DECISION WORKFLOW
Five evidence-preserving steps.
01
Lock the input, endpoint, controls, and exclusions
02
Generate a traceable candidate space
03
Calibrate orthogonal evidence and uncertainty
04
Select a diverse set under experimental constraints
05
Deliver assay-ready files and a data-return schema
CAT SOLUTIONS
4 defined solution packages.
BIO-DIM-001De-immunization
Project-defined target and comparator set
27 → 42 → 8 constructsOpen solution →BIO-DIM-002VHH De-immunization and Humanization
VHH
18 → 36 → 6 constructsOpen solution →BIO-DIM-003AAV T-cell Epitope Optimization
AAV variants
31 → 48 → 10 constructsOpen solution →BIO-DIM-004De-immunization
20–80 aa
12 → 24 → 6 constructsOpen solution →AI Product Atlas
BIO-DIM-001 · Illustrative planning example
De-immunization
Project-defined target and comparator set
Biologics Design research and development
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Target product profile
Define Project-defined target and comparator set, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Structure and sequence preparation
Generate a traceable candidate space appropriate to de-immunized construct design.
candidate-space file and provenance03 · Variant or epitope generation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Safety and developability calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Library or construct selection
Select a diverse, capacity-matched set for Biologics Design research and development.
ranked experimental package06 · Experimental validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Closed-loop redesign
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
BIO-DIM-002 · Illustrative planning example
VHH De-immunization and Humanization
VHH
Biologics Design research and development
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Target product profile
Define VHH, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Structure and sequence preparation
Generate a traceable candidate space appropriate to de-immunized construct design.
candidate-space file and provenance03 · Variant or epitope generation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Safety and developability calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Library or construct selection
Select a diverse, capacity-matched set for Biologics Design research and development.
ranked experimental package06 · Experimental validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Closed-loop redesign
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
BIO-DIM-003 · Illustrative planning example
AAV T-cell Epitope Optimization
AAV variants
Gene Therapy
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Target product profile
Define AAV variants, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Structure and sequence preparation
Generate a traceable candidate space appropriate to de-immunized construct design.
candidate-space file and provenance03 · Variant or epitope generation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Safety and developability calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Library or construct selection
Select a diverse, capacity-matched set for Gene Therapy.
ranked experimental package06 · Experimental validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Closed-loop redesign
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
BIO-DIM-004 · Illustrative planning example
De-immunization
20–80 aa
Biologics Design research and development
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Target product profile
Define 20–80 aa, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Structure and sequence preparation
Generate a traceable candidate space appropriate to de-immunized construct design.
candidate-space file and provenance03 · Variant or epitope generation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Safety and developability calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Library or construct selection
Select a diverse, capacity-matched set for Biologics Design research and development.
ranked experimental package06 · Experimental validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Closed-loop redesign
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
GEN-VAR · PRODUCT LINE
VariantRank-AI
VUS Triage and Validation Queue
Designed to answer one actionable R&D question: which candidates, controls, and validation experiments should advance together?
DECISION WORKFLOW
Five evidence-preserving steps.
01
Lock the input, endpoint, controls, and exclusions
02
Generate a traceable candidate space
03
Calibrate orthogonal evidence and uncertainty
04
Select a diverse set under experimental constraints
05
Deliver assay-ready files and a data-return schema
CAT SOLUTIONS
4 defined solution packages.
GEN-VAR-001Oncology 30 VUS Triage
EGFR/KRAS/TP53/PIK3CA and related targets
486 VUS → 18 → 6Open solution →GEN-VAR-002Inherited Disease panel VUS Triage
CFTR/DMD/HBB/F8/F9 and related targets
324 VUS → 16 → 5/validationOpen solution →GEN-VAR-003BRCA/Lynch
BRCA1/2 + MMR genes
188 VUS → 12 → 4 validationOpen solution →GEN-VAR-004UTR — VUS Triage and Validation Queue
UTR
742 → 20 candidates → 8Open solution →AI Product Atlas
GEN-VAR-001 · Illustrative planning example
Oncology 30 VUS Triage
EGFR/KRAS/TP53/PIK3CA and related targets
Oncology panel
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Versioned sample manifest
Define EGFR/KRAS/TP53/PIK3CA and related targets, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Variant and transcript normalization
Generate a traceable candidate space appropriate to vus triage and validation queue.
candidate-space file and provenance03 · Mechanistic candidate generation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Evidence and uncertainty calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Validation-set selection
Select a diverse, capacity-matched set for Oncology panel.
ranked experimental package06 · Orthogonal assay execution
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Interpretation update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
GEN-VAR-002 · Illustrative planning example
Inherited Disease panel VUS Triage
CFTR/DMD/HBB/F8/F9 and related targets
Inherited Disease
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Versioned sample manifest
Define CFTR/DMD/HBB/F8/F9 and related targets, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Variant and transcript normalization
Generate a traceable candidate space appropriate to vus triage and validation queue.
candidate-space file and provenance03 · Mechanistic candidate generation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Evidence and uncertainty calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Validation-set selection
Select a diverse, capacity-matched set for Inherited Disease.
ranked experimental package06 · Orthogonal assay execution
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Interpretation update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
GEN-VAR-003 · Illustrative planning example
BRCA/Lynch
BRCA1/2 + MMR genes
Oncology
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Versioned sample manifest
Define BRCA1/2 + MMR genes, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Variant and transcript normalization
Generate a traceable candidate space appropriate to vus triage and validation queue.
candidate-space file and provenance03 · Mechanistic candidate generation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Evidence and uncertainty calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Validation-set selection
Select a diverse, capacity-matched set for Oncology.
ranked experimental package06 · Orthogonal assay execution
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Interpretation update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
GEN-VAR-004 · Illustrative planning example
UTR — VUS Triage and Validation Queue
UTR
and Gene Therapy
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Versioned sample manifest
Define UTR, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Variant and transcript normalization
Generate a traceable candidate space appropriate to vus triage and validation queue.
candidate-space file and provenance03 · Mechanistic candidate generation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Evidence and uncertainty calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Validation-set selection
Select a diverse, capacity-matched set for and Gene Therapy.
ranked experimental package06 · Orthogonal assay execution
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Interpretation update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
GEN-PGX · PRODUCT LINE
PGxMap-AI
Pharmacogenomic Interpretation
Designed to answer one actionable R&D question: which candidates, controls, and validation experiments should advance together?
DECISION WORKFLOW
Five evidence-preserving steps.
01
Lock the input, endpoint, controls, and exclusions
02
Generate a traceable candidate space
03
Calibrate orthogonal evidence and uncertainty
04
Select a diverse set under experimental constraints
05
Deliver assay-ready files and a data-return schema
CAT SOLUTIONS
4 defined solution packages.
GEN-PGX-001CYP2D6 Complex Haplotype Interpretation
CYP2D6 SNV/CNV/hybrid
128 samples → 19 → 8Open solution →GEN-PGX-002DPYD Risk Interpretation
DPYD
86 samples → 7 risk → 4Open solution →GEN-PGX-003TPMT/NUDT15
TPMT + NUDT15
112 samples → 9 risk → 3Open solution →GEN-PGX-004HLA Drug-hypersensitivity Interpretation
HLA-B*57:01/*15:02/*58:01
240 samples → 16 risk → 6Open solution →AI Product Atlas
GEN-PGX-001 · Illustrative planning example
CYP2D6 Complex Haplotype Interpretation
CYP2D6 SNV/CNV/hybrid
and
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Versioned sample manifest
Define CYP2D6 SNV/CNV/hybrid, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Variant and transcript normalization
Generate a traceable candidate space appropriate to pharmacogenomic interpretation.
candidate-space file and provenance03 · Mechanistic candidate generation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Evidence and uncertainty calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Validation-set selection
Select a diverse, capacity-matched set for and.
ranked experimental package06 · Orthogonal assay execution
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Interpretation update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
GEN-PGX-002 · Illustrative planning example
DPYD Risk Interpretation
DPYD
Oncology
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Versioned sample manifest
Define DPYD, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Variant and transcript normalization
Generate a traceable candidate space appropriate to pharmacogenomic interpretation.
candidate-space file and provenance03 · Mechanistic candidate generation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Evidence and uncertainty calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Validation-set selection
Select a diverse, capacity-matched set for Oncology.
ranked experimental package06 · Orthogonal assay execution
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Interpretation update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
GEN-PGX-003 · Illustrative planning example
TPMT/NUDT15
TPMT + NUDT15
Genomics & Editing research and development
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Versioned sample manifest
Define TPMT + NUDT15, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Variant and transcript normalization
Generate a traceable candidate space appropriate to pharmacogenomic interpretation.
candidate-space file and provenance03 · Mechanistic candidate generation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Evidence and uncertainty calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Validation-set selection
Select a diverse, capacity-matched set for Genomics & Editing research and development.
ranked experimental package06 · Orthogonal assay execution
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Interpretation update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
GEN-PGX-004 · Illustrative planning example
HLA Drug-hypersensitivity Interpretation
HLA-B*57:01/*15:02/*58:01
Drug
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Versioned sample manifest
Define HLA-B*57:01/*15:02/*58:01, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Variant and transcript normalization
Generate a traceable candidate space appropriate to pharmacogenomic interpretation.
candidate-space file and provenance03 · Mechanistic candidate generation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Evidence and uncertainty calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Validation-set selection
Select a diverse, capacity-matched set for Drug.
ranked experimental package06 · Orthogonal assay execution
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Interpretation update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
GEN-SPL · PRODUCT LINE
SpliceMap-AI
Splicing Variant Mechanism Design
Designed to answer one actionable R&D question: which candidates, controls, and validation experiments should advance together?
DECISION WORKFLOW
Five evidence-preserving steps.
01
Lock the input, endpoint, controls, and exclusions
02
Generate a traceable candidate space
03
Calibrate orthogonal evidence and uncertainty
04
Select a diverse set under experimental constraints
05
Deliver assay-ready files and a data-return schema
CAT SOLUTIONS
4 defined solution packages.
GEN-SPL-001CFTR Splicing VUS
CFTR and
96 → 12 candidates → 6 minigeneOpen solution →GEN-SPL-002DMD Exon-skipping Mechanism
DMD
78/windows → 10 candidates → 5 RT-PCR/ASOOpen solution →GEN-SPL-003BRCA1/2 Splicing VUS
BRCA1/2
132 → 14 candidates → 6 RNA validationOpen solution →GEN-SPL-004SMN2 Splicing Regulation
SMN2 exon 7
44 windows → 8 candidates → 4 ASOOpen solution →AI Product Atlas
GEN-SPL-001 · Illustrative planning example
CFTR Splicing VUS
CFTR and
Genomics & Editing research and development
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Versioned sample manifest
Define CFTR and, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Variant and transcript normalization
Generate a traceable candidate space appropriate to splicing variant mechanism design.
candidate-space file and provenance03 · Mechanistic candidate generation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Evidence and uncertainty calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Validation-set selection
Select a diverse, capacity-matched set for Genomics & Editing research and development.
ranked experimental package06 · Orthogonal assay execution
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Interpretation update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
GEN-SPL-002 · Illustrative planning example
DMD Exon-skipping Mechanism
DMD
DMD
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Versioned sample manifest
Define DMD, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Variant and transcript normalization
Generate a traceable candidate space appropriate to splicing variant mechanism design.
candidate-space file and provenance03 · Mechanistic candidate generation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Evidence and uncertainty calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Validation-set selection
Select a diverse, capacity-matched set for DMD.
ranked experimental package06 · Orthogonal assay execution
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Interpretation update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
GEN-SPL-003 · Illustrative planning example
BRCA1/2 Splicing VUS
BRCA1/2
Oncology
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Versioned sample manifest
Define BRCA1/2, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Variant and transcript normalization
Generate a traceable candidate space appropriate to splicing variant mechanism design.
candidate-space file and provenance03 · Mechanistic candidate generation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Evidence and uncertainty calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Validation-set selection
Select a diverse, capacity-matched set for Oncology.
ranked experimental package06 · Orthogonal assay execution
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Interpretation update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
GEN-SPL-004 · Illustrative planning example
SMN2 Splicing Regulation
SMN2 exon 7
SMA
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Versioned sample manifest
Define SMN2 exon 7, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Variant and transcript normalization
Generate a traceable candidate space appropriate to splicing variant mechanism design.
candidate-space file and provenance03 · Mechanistic candidate generation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Evidence and uncertainty calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Validation-set selection
Select a diverse, capacity-matched set for SMA.
ranked experimental package06 · Orthogonal assay execution
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Interpretation update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
GEN-EDT · PRODUCT LINE
EditGuide-AI
Genome-editing Design Sets
Designed to answer one actionable R&D question: which candidates, controls, and validation experiments should advance together?
DECISION WORKFLOW
Five evidence-preserving steps.
01
Lock the input, endpoint, controls, and exclusions
02
Generate a traceable candidate space
03
Calibrate orthogonal evidence and uncertainty
04
Select a diverse set under experimental constraints
05
Deliver assay-ready files and a data-return schema
CAT SOLUTIONS
4 defined solution packages.
GEN-EDT-001Single-gene CRISPR Knockout
+ cells SNP
186 guide → 12 + 4 controls → 16 wellsOpen solution →GEN-EDT-002ABE/CBE Disease-site Editing
SNV
176 designs → 16 + 4 controlsOpen solution →GEN-EDT-003Prime editing Site Design
SNV/indel
240 pegRNA designs → 18 + 6 controlsOpen solution →GEN-EDT-004CRISPR panel Library
50–500 genes
24,000 guide → 4–6/+Open solution →AI Product Atlas
GEN-EDT-001 · Illustrative planning example
Single-gene CRISPR Knockout
+ cells SNP
validation
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Versioned sample manifest
Define + cells SNP, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Variant and transcript normalization
Generate a traceable candidate space appropriate to genome-editing design sets.
candidate-space file and provenance03 · Mechanistic candidate generation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Evidence and uncertainty calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Validation-set selection
Select a diverse, capacity-matched set for validation.
ranked experimental package06 · Orthogonal assay execution
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Interpretation update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
GEN-EDT-002 · Illustrative planning example
ABE/CBE Disease-site Editing
SNV
Genomics & Editing research and development
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Versioned sample manifest
Define SNV, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Variant and transcript normalization
Generate a traceable candidate space appropriate to genome-editing design sets.
candidate-space file and provenance03 · Mechanistic candidate generation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Evidence and uncertainty calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Validation-set selection
Select a diverse, capacity-matched set for Genomics & Editing research and development.
ranked experimental package06 · Orthogonal assay execution
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Interpretation update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
GEN-EDT-003 · Illustrative planning example
Prime editing Site Design
SNV/indel
Genomics & Editing research and development
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Versioned sample manifest
Define SNV/indel, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Variant and transcript normalization
Generate a traceable candidate space appropriate to genome-editing design sets.
candidate-space file and provenance03 · Mechanistic candidate generation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Evidence and uncertainty calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Validation-set selection
Select a diverse, capacity-matched set for Genomics & Editing research and development.
ranked experimental package06 · Orthogonal assay execution
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Interpretation update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
GEN-EDT-004 · Illustrative planning example
CRISPR panel Library
50–500 genes
Genomics & Editing research and development
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Versioned sample manifest
Define 50–500 genes, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Variant and transcript normalization
Generate a traceable candidate space appropriate to genome-editing design sets.
candidate-space file and provenance03 · Mechanistic candidate generation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Evidence and uncertainty calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Validation-set selection
Select a diverse, capacity-matched set for Genomics & Editing research and development.
ranked experimental package06 · Orthogonal assay execution
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Interpretation update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
GEN-FUS · PRODUCT LINE
FusionMap-AI
Fusion Breakpoint Interpretation
Designed to answer one actionable R&D question: which candidates, controls, and validation experiments should advance together?
DECISION WORKFLOW
Five evidence-preserving steps.
01
Lock the input, endpoint, controls, and exclusions
02
Generate a traceable candidate space
03
Calibrate orthogonal evidence and uncertainty
04
Select a diverse set under experimental constraints
05
Deliver assay-ready files and a data-return schema
CAT SOLUTIONS
4 defined solution packages.
GEN-FUS-001ALK/ROS1/RET Kinase Fusion
Lung Cancer Kinase Fusion
73 → 11 candidates → 5 validationOpen solution →GEN-FUS-002NTRK/FGFR
NTRK1-3/FGFR1-4
58 → 9 candidates → 4 validationOpen solution →GEN-FUS-003BCR-ABL1 Breakpoint Interpretation
BCR-ABL1 isoforms
26 designs → 6 → 3 validationOpen solution →GEN-FUS-004EWSR1
EWSR1 partner fusions
44 → 8 candidates → 4 validationOpen solution →AI Product Atlas
GEN-FUS-001 · Illustrative planning example
ALK/ROS1/RET Kinase Fusion
Lung Cancer Kinase Fusion
Lung Cancer panel
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Versioned sample manifest
Define Lung Cancer Kinase Fusion, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Variant and transcript normalization
Generate a traceable candidate space appropriate to fusion breakpoint interpretation.
candidate-space file and provenance03 · Mechanistic candidate generation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Evidence and uncertainty calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Validation-set selection
Select a diverse, capacity-matched set for Lung Cancer panel.
ranked experimental package06 · Orthogonal assay execution
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Interpretation update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
GEN-FUS-002 · Illustrative planning example
NTRK/FGFR
NTRK1-3/FGFR1-4
panel
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Versioned sample manifest
Define NTRK1-3/FGFR1-4, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Variant and transcript normalization
Generate a traceable candidate space appropriate to fusion breakpoint interpretation.
candidate-space file and provenance03 · Mechanistic candidate generation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Evidence and uncertainty calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Validation-set selection
Select a diverse, capacity-matched set for panel.
ranked experimental package06 · Orthogonal assay execution
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Interpretation update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
GEN-FUS-003 · Illustrative planning example
BCR-ABL1 Breakpoint Interpretation
BCR-ABL1 isoforms
Oncology
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Versioned sample manifest
Define BCR-ABL1 isoforms, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Variant and transcript normalization
Generate a traceable candidate space appropriate to fusion breakpoint interpretation.
candidate-space file and provenance03 · Mechanistic candidate generation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Evidence and uncertainty calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Validation-set selection
Select a diverse, capacity-matched set for Oncology.
ranked experimental package06 · Orthogonal assay execution
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Interpretation update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
GEN-FUS-004 · Illustrative planning example
EWSR1
EWSR1 partner fusions
Genomics & Editing research and development
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Versioned sample manifest
Define EWSR1 partner fusions, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Variant and transcript normalization
Generate a traceable candidate space appropriate to fusion breakpoint interpretation.
candidate-space file and provenance03 · Mechanistic candidate generation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Evidence and uncertainty calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Validation-set selection
Select a diverse, capacity-matched set for Genomics & Editing research and development.
ranked experimental package06 · Orthogonal assay execution
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Interpretation update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
GEN-NEO · PRODUCT LINE
Neoantigen-AI
Neoantigen Portfolio and Construct Design
Designed to answer one actionable R&D question: which candidates, controls, and validation experiments should advance together?
DECISION WORKFLOW
Five evidence-preserving steps.
01
Lock the input, endpoint, controls, and exclusions
02
Generate a traceable candidate space
03
Calibrate orthogonal evidence and uncertainty
04
Select a diverse set under experimental constraints
05
Deliver assay-ready files and a data-return schema
CAT SOLUTIONS
4 defined solution packages.
GEN-NEO-001Personalized Melanoma Neoantigen mRNA Payload Design
paired tumor/normal WES + tumor RNA + HLA-I/II
388 somatic mutations → 26,118 peptide–HLA predictions → 34 neoantigens → 1 mRNAOpen solution →GEN-NEO-002TCR-T Neoantigen Targets
patients/HLA and
1,240 candidates → 18 → 12 tetramerOpen solution →GEN-NEO-003Shared Hotspot Neoantigen Library
KRAS/TP53/PIK3CA and related targets
3,600 –HLA vs. → 48 candidatesOpen solution →GEN-NEO-004HLA-II CD4 Neoantigen Portfolio
13–25mer
2,180 → 20 candidates → 8 CD4Open solution →AI Product Atlas
GEN-NEO-001 · Public-data case study
Personalized Melanoma Neoantigen mRNA Payload Design
paired tumor/normal WES + tumor RNA + HLA-I/II
Personalized Cancer Vaccine
Personalized melanoma neoantigen mRNA payload
A research-use workflow connects paired tumor/normal sequencing, tumor expression, HLA-I/HLA-II presentation, immunogenicity, clonality, and manufacturability without collapsing them into a single opaque score.
388 somatic mutations→265 passed gating→26,118 peptide–HLA predictions→397 strong binders→34 selected neoantigens→1 × 2,550-nt mRNA
Selection logic
Balance HLA-I/HLA-II coverage, mutation clonality, tumor expression, peptide diversity, manufacturability, and redundancy.
Construct logic
Optimize ordering and linkers to reduce junctional epitopes while retaining processing and payload stability.
Evidence boundary
Computed ranking is not immune-response validation. Candidate-specific tetramer, ELISpot, or T-cell assays remain required.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Versioned sample manifest
Define paired tumor/normal WES + tumor RNA + HLA-I/II, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Variant and transcript normalization
Generate a traceable candidate space appropriate to neoantigen portfolio and construct design.
candidate-space file and provenance03 · Mechanistic candidate generation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Evidence and uncertainty calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Validation-set selection
Select a diverse, capacity-matched set for Personalized Cancer Vaccine.
ranked experimental package06 · Orthogonal assay execution
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Interpretation update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
GEN-NEO-002 · Illustrative planning example
TCR-T Neoantigen Targets
patients/HLA and
TCR-T
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Versioned sample manifest
Define patients/HLA and, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Variant and transcript normalization
Generate a traceable candidate space appropriate to neoantigen portfolio and construct design.
candidate-space file and provenance03 · Mechanistic candidate generation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Evidence and uncertainty calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Validation-set selection
Select a diverse, capacity-matched set for TCR-T.
ranked experimental package06 · Orthogonal assay execution
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Interpretation update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
GEN-NEO-003 · Illustrative planning example
Shared Hotspot Neoantigen Library
KRAS/TP53/PIK3CA and related targets
Vaccine/TCR
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Versioned sample manifest
Define KRAS/TP53/PIK3CA and related targets, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Variant and transcript normalization
Generate a traceable candidate space appropriate to neoantigen portfolio and construct design.
candidate-space file and provenance03 · Mechanistic candidate generation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Evidence and uncertainty calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Validation-set selection
Select a diverse, capacity-matched set for Vaccine/TCR.
ranked experimental package06 · Orthogonal assay execution
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Interpretation update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
GEN-NEO-004 · Illustrative planning example
HLA-II CD4 Neoantigen Portfolio
13–25mer
Vaccine
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Versioned sample manifest
Define 13–25mer, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Variant and transcript normalization
Generate a traceable candidate space appropriate to neoantigen portfolio and construct design.
candidate-space file and provenance03 · Mechanistic candidate generation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Evidence and uncertainty calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Validation-set selection
Select a diverse, capacity-matched set for Vaccine.
ranked experimental package06 · Orthogonal assay execution
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Interpretation update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
SC-ATL · PRODUCT LINE
CellAtlas-AI
Disease Reference Atlases
Designed to answer one actionable R&D question: which candidates, controls, and validation experiments should advance together?
DECISION WORKFLOW
Five evidence-preserving steps.
01
Lock the input, endpoint, controls, and exclusions
02
Generate a traceable candidate space
03
Calibrate orthogonal evidence and uncertainty
04
Select a diverse set under experimental constraints
05
Deliver assay-ready files and a data-return schema
CAT SOLUTIONS
5 defined solution packages.
SC-ATL-001NSCLC TME Reference Atlas
NSCLC tumor microenvironment
1.2M cells → 42 states → 3/validationOpen solution →SC-ATL-002Colorectal Cancer Reference Atlas
CRC Oncology
433,804 candidate cells → donor-aware reference model → customer mapping and validation subpopulationsOpen solution →SC-ATL-003Liver Cancer Reference Atlas
HCC/iCCA
760k cells → 36 states → 3 validationOpen solution →SC-ATL-004Rheumatoid Synovium Atlas
RA synovium
420k cells → 31 states → 5Open solution →SC-ATL-005IBD Gut Mucosa Atlas
Crohn/UC
680k cells → 40 states → 4Open solution →AI Product Atlas
SC-ATL-001 · Illustrative planning example
NSCLC TME Reference Atlas
NSCLC tumor microenvironment
Lung Cancer
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Cohort and ontology lock
Define NSCLC tumor microenvironment, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · QC and donor-aware preprocessing
Generate a traceable candidate space appropriate to disease reference atlases.
candidate-space file and provenance03 · Reference mapping or state discovery
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Robustness and OOD audit
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Marker/model/clone selection
Select a diverse, capacity-matched set for Lung Cancer.
ranked experimental package06 · Orthogonal validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Atlas and decision update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
SC-ATL-002 · Public-data case study
Colorectal Cancer Reference Atlas
CRC Oncology
Colorectal Cancer
Donor-aware disease reference mapping
Public single-cell cohorts are harmonized without erasing donor structure. Customer cells are mapped with state probabilities, out-of-distribution flags, and an orthogonal marker plan.
Reference build
Version donors, tissues, assays, diagnoses, and ontologies before integration.
Mapping output
State probabilities, nearest references, unknown-cell flags, and candidate validation markers.
Evidence boundary
Reference labels guide interpretation; they do not replace pathology or functional validation.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Cohort and ontology lock
Define CRC Oncology, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · QC and donor-aware preprocessing
Generate a traceable candidate space appropriate to disease reference atlases.
candidate-space file and provenance03 · Reference mapping or state discovery
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Robustness and OOD audit
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Marker/model/clone selection
Select a diverse, capacity-matched set for Colorectal Cancer.
ranked experimental package06 · Orthogonal validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Atlas and decision update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
SC-ATL-003 · Illustrative planning example
Liver Cancer Reference Atlas
HCC/iCCA
Liver Cancer
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Cohort and ontology lock
Define HCC/iCCA, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · QC and donor-aware preprocessing
Generate a traceable candidate space appropriate to disease reference atlases.
candidate-space file and provenance03 · Reference mapping or state discovery
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Robustness and OOD audit
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Marker/model/clone selection
Select a diverse, capacity-matched set for Liver Cancer.
ranked experimental package06 · Orthogonal validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Atlas and decision update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
SC-ATL-004 · Illustrative planning example
Rheumatoid Synovium Atlas
RA synovium
Single-cell & Tissue research and development
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Cohort and ontology lock
Define RA synovium, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · QC and donor-aware preprocessing
Generate a traceable candidate space appropriate to disease reference atlases.
candidate-space file and provenance03 · Reference mapping or state discovery
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Robustness and OOD audit
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Marker/model/clone selection
Select a diverse, capacity-matched set for Single-cell & Tissue research and development.
ranked experimental package06 · Orthogonal validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Atlas and decision update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
SC-ATL-005 · Illustrative planning example
IBD Gut Mucosa Atlas
Crohn/UC
Single-cell & Tissue research and development
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Cohort and ontology lock
Define Crohn/UC, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · QC and donor-aware preprocessing
Generate a traceable candidate space appropriate to disease reference atlases.
candidate-space file and provenance03 · Reference mapping or state discovery
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Robustness and OOD audit
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Marker/model/clone selection
Select a diverse, capacity-matched set for Single-cell & Tissue research and development.
ranked experimental package06 · Orthogonal validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Atlas and decision update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
SC-SAF · PRODUCT LINE
SafetyMap-AI
Normal-tissue Target Safety Maps
Designed to answer one actionable R&D question: which candidates, controls, and validation experiments should advance together?
DECISION WORKFLOW
Five evidence-preserving steps.
01
Lock the input, endpoint, controls, and exclusions
02
Generate a traceable candidate space
03
Calibrate orthogonal evidence and uncertainty
04
Select a diverse set under experimental constraints
05
Deliver assay-ready files and a data-return schema
CAT SOLUTIONS
5 defined solution packages.
SC-SAF-001CLDN18.2 Safety Atlas
CLDN18.2
152 cells → 7 sentinel risks → 5 validationOpen solution →SC-SAF-002TROP2 Safety Atlas
TROP2/TACSTD2
152 cells → 9 sentinel risks → 6 validationOpen solution →SC-SAF-003B7-H3 Safety Atlas
CD276/B7-H3
152 cells → 8 sentinel risks → 5 validationOpen solution →SC-SAF-004GPRC5D Safety Atlas
GPRC5D
152 cells → 6 sentinel risks → 5 validationOpen solution →SC-SAF-005DLL3 Safety Atlas
DLL3
152 cells → 5 sentinel risks → 4 validationOpen solution →AI Product Atlas
SC-SAF-001 · Illustrative planning example
CLDN18.2 Safety Atlas
CLDN18.2
CAR-T/ADC
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Cohort and ontology lock
Define CLDN18.2, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · QC and donor-aware preprocessing
Generate a traceable candidate space appropriate to normal-tissue target safety maps.
candidate-space file and provenance03 · Reference mapping or state discovery
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Robustness and OOD audit
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Marker/model/clone selection
Select a diverse, capacity-matched set for CAR-T/ADC.
ranked experimental package06 · Orthogonal validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Atlas and decision update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
SC-SAF-002 · Illustrative planning example
TROP2 Safety Atlas
TROP2/TACSTD2
ADC/TCE
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Cohort and ontology lock
Define TROP2/TACSTD2, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · QC and donor-aware preprocessing
Generate a traceable candidate space appropriate to normal-tissue target safety maps.
candidate-space file and provenance03 · Reference mapping or state discovery
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Robustness and OOD audit
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Marker/model/clone selection
Select a diverse, capacity-matched set for ADC/TCE.
ranked experimental package06 · Orthogonal validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Atlas and decision update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
SC-SAF-003 · Illustrative planning example
B7-H3 Safety Atlas
CD276/B7-H3
ADC/CAR-T
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Cohort and ontology lock
Define CD276/B7-H3, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · QC and donor-aware preprocessing
Generate a traceable candidate space appropriate to normal-tissue target safety maps.
candidate-space file and provenance03 · Reference mapping or state discovery
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Robustness and OOD audit
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Marker/model/clone selection
Select a diverse, capacity-matched set for ADC/CAR-T.
ranked experimental package06 · Orthogonal validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Atlas and decision update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
SC-SAF-004 · Illustrative planning example
GPRC5D Safety Atlas
GPRC5D
Single-cell & Tissue research and development
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Cohort and ontology lock
Define GPRC5D, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · QC and donor-aware preprocessing
Generate a traceable candidate space appropriate to normal-tissue target safety maps.
candidate-space file and provenance03 · Reference mapping or state discovery
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Robustness and OOD audit
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Marker/model/clone selection
Select a diverse, capacity-matched set for Single-cell & Tissue research and development.
ranked experimental package06 · Orthogonal validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Atlas and decision update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Cohort and ontology lock
Define DLL3, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · QC and donor-aware preprocessing
Generate a traceable candidate space appropriate to normal-tissue target safety maps.
candidate-space file and provenance03 · Reference mapping or state discovery
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Robustness and OOD audit
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Marker/model/clone selection
Select a diverse, capacity-matched set for SCLC.
ranked experimental package06 · Orthogonal validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Atlas and decision update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
SC-MOD · PRODUCT LINE
ModelMatch-AI
Cell-line and Organoid Selection
Designed to answer one actionable R&D question: which candidates, controls, and validation experiments should advance together?
DECISION WORKFLOW
Five evidence-preserving steps.
01
Lock the input, endpoint, controls, and exclusions
02
Generate a traceable candidate space
03
Calibrate orthogonal evidence and uncertainty
04
Select a diverse set under experimental constraints
05
Deliver assay-ready files and a data-return schema
CAT SOLUTIONS
4 defined solution packages.
SC-MOD-001KRAS Model Set
KRAS mutant backgrounds
64 models → 4 models + 2 controlsOpen solution →SC-MOD-002EGFR Model Set
EGFR L858R/T790M/C797S
52 models → 5 models + 2 controlsOpen solution →SC-MOD-003cells Model Set
T/NK/myeloid models
46 models → 4 models + 3 controlsOpen solution →SC-MOD-004Model Set
hepatocyte/organoid models
38 models → 4 models + 2 controlsOpen solution →AI Product Atlas
SC-MOD-001 · Illustrative planning example
KRAS Model Set
KRAS mutant backgrounds
KRAS
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Cohort and ontology lock
Define KRAS mutant backgrounds, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · QC and donor-aware preprocessing
Generate a traceable candidate space appropriate to cell-line and organoid selection.
candidate-space file and provenance03 · Reference mapping or state discovery
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Robustness and OOD audit
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Marker/model/clone selection
Select a diverse, capacity-matched set for KRAS.
ranked experimental package06 · Orthogonal validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Atlas and decision update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
SC-MOD-002 · Illustrative planning example
EGFR Model Set
EGFR L858R/T790M/C797S
Lung Cancer
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Cohort and ontology lock
Define EGFR L858R/T790M/C797S, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · QC and donor-aware preprocessing
Generate a traceable candidate space appropriate to cell-line and organoid selection.
candidate-space file and provenance03 · Reference mapping or state discovery
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Robustness and OOD audit
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Marker/model/clone selection
Select a diverse, capacity-matched set for Lung Cancer.
ranked experimental package06 · Orthogonal validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Atlas and decision update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
SC-MOD-003 · Illustrative planning example
cells Model Set
T/NK/myeloid models
Single-cell & Tissue research and development
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Cohort and ontology lock
Define T/NK/myeloid models, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · QC and donor-aware preprocessing
Generate a traceable candidate space appropriate to cell-line and organoid selection.
candidate-space file and provenance03 · Reference mapping or state discovery
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Robustness and OOD audit
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Marker/model/clone selection
Select a diverse, capacity-matched set for Single-cell & Tissue research and development.
ranked experimental package06 · Orthogonal validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Atlas and decision update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
SC-MOD-004 · Illustrative planning example
Model Set
hepatocyte/organoid models
Single-cell & Tissue research and development
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Cohort and ontology lock
Define hepatocyte/organoid models, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · QC and donor-aware preprocessing
Generate a traceable candidate space appropriate to cell-line and organoid selection.
candidate-space file and provenance03 · Reference mapping or state discovery
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Robustness and OOD audit
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Marker/model/clone selection
Select a diverse, capacity-matched set for Single-cell & Tissue research and development.
ranked experimental package06 · Orthogonal validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Atlas and decision update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
SC-DEC · PRODUCT LINE
ClinicalDeconv-AI
Single-cell to Clinical Cohort Translation
Designed to answer one actionable R&D question: which candidates, controls, and validation experiments should advance together?
DECISION WORKFLOW
Five evidence-preserving steps.
01
Lock the input, endpoint, controls, and exclusions
02
Generate a traceable candidate space
03
Calibrate orthogonal evidence and uncertainty
04
Select a diverse set under experimental constraints
05
Deliver assay-ready files and a data-return schema
CAT SOLUTIONS
4 defined solution packages.
SC-DEC-001NSCLC bulk Immune Deconvolution
NSCLC FFPE/RNA-seq
46 → 9 → 4 validationdesignsOpen solution →SC-DEC-002Breast Cancer TME Deconvolution
breast cancer cohorts
44 → 8 → 4 validationdesignsOpen solution →SC-DEC-003IBD Treatment-response Signature
IBD mucosa bulk
38 → 7 → 3 validationdesignsOpen solution →SC-DEC-004MASH Liver Cell-composition Signature
MASH liver bulk
35 → 6 → 3 validationdesignsOpen solution →AI Product Atlas
SC-DEC-001 · Illustrative planning example
NSCLC bulk Immune Deconvolution
NSCLC FFPE/RNA-seq
Immunotherapy biomarker
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Cohort and ontology lock
Define NSCLC FFPE/RNA-seq, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · QC and donor-aware preprocessing
Generate a traceable candidate space appropriate to single-cell to clinical cohort translation.
candidate-space file and provenance03 · Reference mapping or state discovery
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Robustness and OOD audit
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Marker/model/clone selection
Select a diverse, capacity-matched set for Immunotherapy biomarker.
ranked experimental package06 · Orthogonal validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Atlas and decision update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
SC-DEC-002 · Illustrative planning example
Breast Cancer TME Deconvolution
breast cancer cohorts
Breast Cancer
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Cohort and ontology lock
Define breast cancer cohorts, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · QC and donor-aware preprocessing
Generate a traceable candidate space appropriate to single-cell to clinical cohort translation.
candidate-space file and provenance03 · Reference mapping or state discovery
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Robustness and OOD audit
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Marker/model/clone selection
Select a diverse, capacity-matched set for Breast Cancer.
ranked experimental package06 · Orthogonal validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Atlas and decision update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
SC-DEC-003 · Illustrative planning example
IBD Treatment-response Signature
IBD mucosa bulk
Single-cell & Tissue research and development
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Cohort and ontology lock
Define IBD mucosa bulk, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · QC and donor-aware preprocessing
Generate a traceable candidate space appropriate to single-cell to clinical cohort translation.
candidate-space file and provenance03 · Reference mapping or state discovery
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Robustness and OOD audit
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Marker/model/clone selection
Select a diverse, capacity-matched set for Single-cell & Tissue research and development.
ranked experimental package06 · Orthogonal validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Atlas and decision update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
SC-DEC-004 · Illustrative planning example
MASH Liver Cell-composition Signature
MASH liver bulk
Single-cell & Tissue research and development
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Cohort and ontology lock
Define MASH liver bulk, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · QC and donor-aware preprocessing
Generate a traceable candidate space appropriate to single-cell to clinical cohort translation.
candidate-space file and provenance03 · Reference mapping or state discovery
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Robustness and OOD audit
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Marker/model/clone selection
Select a diverse, capacity-matched set for Single-cell & Tissue research and development.
ranked experimental package06 · Orthogonal validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Atlas and decision update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
SC-AUD · PRODUCT LINE
ModelAudit-AI
Single-cell Foundation-model Audit
Designed to answer one actionable R&D question: which candidates, controls, and validation experiments should advance together?
DECISION WORKFLOW
Five evidence-preserving steps.
01
Lock the input, endpoint, controls, and exclusions
02
Generate a traceable candidate space
03
Calibrate orthogonal evidence and uncertainty
04
Select a diverse set under experimental constraints
05
Deliver assay-ready files and a data-return schema
CAT SOLUTIONS
4 defined solution packages.
AI Product Atlas
SC-AUD-001 · Illustrative planning example
cells Model Audit
annotation task
cells
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Cohort and ontology lock
Define annotation task, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · QC and donor-aware preprocessing
Generate a traceable candidate space appropriate to single-cell foundation-model audit.
candidate-space file and provenance03 · Reference mapping or state discovery
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Robustness and OOD audit
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Marker/model/clone selection
Select a diverse, capacity-matched set for cells.
ranked experimental package06 · Orthogonal validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Atlas and decision update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
SC-AUD-002 · Illustrative planning example
Model Audit
integration task
Single-cell & Tissue research and development
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Cohort and ontology lock
Define integration task, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · QC and donor-aware preprocessing
Generate a traceable candidate space appropriate to single-cell foundation-model audit.
candidate-space file and provenance03 · Reference mapping or state discovery
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Robustness and OOD audit
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Marker/model/clone selection
Select a diverse, capacity-matched set for Single-cell & Tissue research and development.
ranked experimental package06 · Orthogonal validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Atlas and decision update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
SC-AUD-003 · Illustrative planning example
Model Audit
perturbation task
cells
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Cohort and ontology lock
Define perturbation task, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · QC and donor-aware preprocessing
Generate a traceable candidate space appropriate to single-cell foundation-model audit.
candidate-space file and provenance03 · Reference mapping or state discovery
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Robustness and OOD audit
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Marker/model/clone selection
Select a diverse, capacity-matched set for cells.
ranked experimental package06 · Orthogonal validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Atlas and decision update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
SC-AUD-004 · Illustrative planning example
cells Detection Audit
rare-cell task
Single-cell & Tissue research and development
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Cohort and ontology lock
Define rare-cell task, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · QC and donor-aware preprocessing
Generate a traceable candidate space appropriate to single-cell foundation-model audit.
candidate-space file and provenance03 · Reference mapping or state discovery
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Robustness and OOD audit
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Marker/model/clone selection
Select a diverse, capacity-matched set for Single-cell & Tissue research and development.
ranked experimental package06 · Orthogonal validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Atlas and decision update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
SC-REP · PRODUCT LINE
Repertoire-AI
TCR/BCR Clone Selection
Designed to answer one actionable R&D question: which candidates, controls, and validation experiments should advance together?
DECISION WORKFLOW
Five evidence-preserving steps.
01
Lock the input, endpoint, controls, and exclusions
02
Generate a traceable candidate space
03
Calibrate orthogonal evidence and uncertainty
04
Select a diverse set under experimental constraints
05
Deliver assay-ready files and a data-return schema
CAT SOLUTIONS
4 defined solution packages.
SC-REP-001ICI Treatment-associated TCRs
pre/post ICI scRNA+TCR
8,420 → 96 candidates → 18 TCROpen solution →SC-REP-002CAR-T Clonal Monitoring
CAR-T longitudinal VDJ
3,600 → 42 → 12 markerOpen solution →SC-REP-003BCR Clone Selection
RA/SLE BCR
6,200 → 84 candidates → 16 BCROpen solution →SC-REP-004Virus-antigen-specific TCRs
viral antigen + HLA
4,800 → 72 candidates → 20 TCROpen solution →AI Product Atlas
SC-REP-001 · Illustrative planning example
ICI Treatment-associated TCRs
pre/post ICI scRNA+TCR
Immunotherapy
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Cohort and ontology lock
Define pre/post ICI scRNA+TCR, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · QC and donor-aware preprocessing
Generate a traceable candidate space appropriate to tcr/bcr clone selection.
candidate-space file and provenance03 · Reference mapping or state discovery
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Robustness and OOD audit
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Marker/model/clone selection
Select a diverse, capacity-matched set for Immunotherapy.
ranked experimental package06 · Orthogonal validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Atlas and decision update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
SC-REP-002 · Illustrative planning example
CAR-T Clonal Monitoring
CAR-T longitudinal VDJ
cells
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Cohort and ontology lock
Define CAR-T longitudinal VDJ, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · QC and donor-aware preprocessing
Generate a traceable candidate space appropriate to tcr/bcr clone selection.
candidate-space file and provenance03 · Reference mapping or state discovery
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Robustness and OOD audit
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Marker/model/clone selection
Select a diverse, capacity-matched set for cells.
ranked experimental package06 · Orthogonal validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Atlas and decision update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
SC-REP-003 · Illustrative planning example
BCR Clone Selection
RA/SLE BCR
Single-cell & Tissue research and development
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Cohort and ontology lock
Define RA/SLE BCR, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · QC and donor-aware preprocessing
Generate a traceable candidate space appropriate to tcr/bcr clone selection.
candidate-space file and provenance03 · Reference mapping or state discovery
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Robustness and OOD audit
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Marker/model/clone selection
Select a diverse, capacity-matched set for Single-cell & Tissue research and development.
ranked experimental package06 · Orthogonal validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Atlas and decision update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
SC-REP-004 · Illustrative planning example
Virus-antigen-specific TCRs
viral antigen + HLA
and Vaccine
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Cohort and ontology lock
Define viral antigen + HLA, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · QC and donor-aware preprocessing
Generate a traceable candidate space appropriate to tcr/bcr clone selection.
candidate-space file and provenance03 · Reference mapping or state discovery
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Robustness and OOD audit
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Marker/model/clone selection
Select a diverse, capacity-matched set for and Vaccine.
ranked experimental package06 · Orthogonal validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Atlas and decision update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
NA-SIR · PRODUCT LINE
siRNASelect-AI
siRNA Portfolio Design
Designed to answer one actionable R&D question: which candidates, controls, and validation experiments should advance together?
DECISION WORKFLOW
Five evidence-preserving steps.
01
Lock the input, endpoint, controls, and exclusions
02
Generate a traceable candidate space
03
Calibrate orthogonal evidence and uncertainty
04
Select a diverse set under experimental constraints
05
Deliver assay-ready files and a data-return schema
CAT SOLUTIONS
5 defined solution packages.
NA-SIR-001PCSK9 siRNA Design
PCSK9
2,146 windows → 84 high-activity candidates → 10 designsOpen solution →NA-SIR-002ANGPTL3 siRNA Design
ANGPTL3
1,988 windows → 76 high-activity candidates → 10 designsOpen solution →NA-SIR-003LPA siRNA Design
LPA
2,420 windows → 92 high-activity candidates → 12 designsOpen solution →NA-SIR-004TTR siRNA Design
TTR
1,640 windows → 68 high-activity candidates → 8 designsOpen solution →NA-SIR-005HBV Pan-genotype siRNA
HBV conserved regions
3,200 windows → 110 conserved candidates → 12 designsOpen solution →AI Product Atlas
NA-SIR-001 · Illustrative planning example
PCSK9 siRNA Design
PCSK9
Nucleic Acid Therapeutics research and development
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Sequence and modality specification
Define PCSK9, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Candidate enumeration
Generate a traceable candidate space appropriate to sirna portfolio design.
candidate-space file and provenance03 · Efficacy and off-target calibration
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Chemistry and construct optimization
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Balanced panel selection
Select a diverse, capacity-matched set for Nucleic Acid Therapeutics research and development.
ranked experimental package06 · Wet-lab validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Sequence–performance learning
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
NA-SIR-002 · Illustrative planning example
ANGPTL3 siRNA Design
ANGPTL3
Nucleic Acid Therapeutics research and development
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Sequence and modality specification
Define ANGPTL3, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Candidate enumeration
Generate a traceable candidate space appropriate to sirna portfolio design.
candidate-space file and provenance03 · Efficacy and off-target calibration
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Chemistry and construct optimization
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Balanced panel selection
Select a diverse, capacity-matched set for Nucleic Acid Therapeutics research and development.
ranked experimental package06 · Wet-lab validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Sequence–performance learning
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
NA-SIR-003 · Illustrative planning example
LPA siRNA Design
LPA
Nucleic Acid Therapeutics research and development
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Sequence and modality specification
Define LPA, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Candidate enumeration
Generate a traceable candidate space appropriate to sirna portfolio design.
candidate-space file and provenance03 · Efficacy and off-target calibration
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Chemistry and construct optimization
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Balanced panel selection
Select a diverse, capacity-matched set for Nucleic Acid Therapeutics research and development.
ranked experimental package06 · Wet-lab validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Sequence–performance learning
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
NA-SIR-004 · Illustrative planning example
TTR siRNA Design
TTR
Nucleic Acid Therapeutics research and development
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Sequence and modality specification
Define TTR, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Candidate enumeration
Generate a traceable candidate space appropriate to sirna portfolio design.
candidate-space file and provenance03 · Efficacy and off-target calibration
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Chemistry and construct optimization
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Balanced panel selection
Select a diverse, capacity-matched set for Nucleic Acid Therapeutics research and development.
ranked experimental package06 · Wet-lab validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Sequence–performance learning
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
NA-SIR-005 · Illustrative planning example
HBV Pan-genotype siRNA
HBV conserved regions
Nucleic Acid Therapeutics research and development
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Sequence and modality specification
Define HBV conserved regions, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Candidate enumeration
Generate a traceable candidate space appropriate to sirna portfolio design.
candidate-space file and provenance03 · Efficacy and off-target calibration
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Chemistry and construct optimization
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Balanced panel selection
Select a diverse, capacity-matched set for Nucleic Acid Therapeutics research and development.
ranked experimental package06 · Wet-lab validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Sequence–performance learning
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
NA-ASO · PRODUCT LINE
ASOSelect-AI
ASO Gapmer and Splice Design
Designed to answer one actionable R&D question: which candidates, controls, and validation experiments should advance together?
DECISION WORKFLOW
Five evidence-preserving steps.
01
Lock the input, endpoint, controls, and exclusions
02
Generate a traceable candidate space
03
Calibrate orthogonal evidence and uncertainty
04
Select a diverse set under experimental constraints
05
Deliver assay-ready files and a data-return schema
CAT SOLUTIONS
5 defined solution packages.
NA-ASO-001SOD1 Gapmer Design
SOD1
842 windows → 42 feasible ASOs → 10 designsOpen solution →NA-ASO-002HTT Allele-selective ASO
HTT SNP-linked windows
1,120 windows → 36 and related targets candidates → 10 designsOpen solution →NA-ASO-003SMN2 Splice-switching ASO
SMN2 exon 7
628 windows → 38 feasible ASOs → 10 + 4 controlsOpen solution →NA-ASO-004DMD Exon-skipping ASO
DMD selected exons
760 windows → 44 feasible ASOs → 12 designsOpen solution →NA-ASO-005MAPT Splice/Knockdown ASO
MAPT isoforms
920 windows → 40 candidates → 10 designsOpen solution →AI Product Atlas
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Sequence and modality specification
Define SOD1, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Candidate enumeration
Generate a traceable candidate space appropriate to aso gapmer and splice design.
candidate-space file and provenance03 · Efficacy and off-target calibration
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Chemistry and construct optimization
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Balanced panel selection
Select a diverse, capacity-matched set for ALS.
ranked experimental package06 · Wet-lab validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Sequence–performance learning
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
NA-ASO-002 · Illustrative planning example
HTT Allele-selective ASO
HTT SNP-linked windows
Huntington
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Sequence and modality specification
Define HTT SNP-linked windows, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Candidate enumeration
Generate a traceable candidate space appropriate to aso gapmer and splice design.
candidate-space file and provenance03 · Efficacy and off-target calibration
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Chemistry and construct optimization
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Balanced panel selection
Select a diverse, capacity-matched set for Huntington.
ranked experimental package06 · Wet-lab validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Sequence–performance learning
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
NA-ASO-003 · Illustrative planning example
SMN2 Splice-switching ASO
SMN2 exon 7
SMA
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Sequence and modality specification
Define SMN2 exon 7, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Candidate enumeration
Generate a traceable candidate space appropriate to aso gapmer and splice design.
candidate-space file and provenance03 · Efficacy and off-target calibration
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Chemistry and construct optimization
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Balanced panel selection
Select a diverse, capacity-matched set for SMA.
ranked experimental package06 · Wet-lab validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Sequence–performance learning
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
NA-ASO-004 · Illustrative planning example
DMD Exon-skipping ASO
DMD selected exons
DMD
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Sequence and modality specification
Define DMD selected exons, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Candidate enumeration
Generate a traceable candidate space appropriate to aso gapmer and splice design.
candidate-space file and provenance03 · Efficacy and off-target calibration
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Chemistry and construct optimization
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Balanced panel selection
Select a diverse, capacity-matched set for DMD.
ranked experimental package06 · Wet-lab validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Sequence–performance learning
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
NA-ASO-005 · Illustrative planning example
MAPT Splice/Knockdown ASO
MAPT isoforms
Neuro
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Sequence and modality specification
Define MAPT isoforms, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Candidate enumeration
Generate a traceable candidate space appropriate to aso gapmer and splice design.
candidate-space file and provenance03 · Efficacy and off-target calibration
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Chemistry and construct optimization
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Balanced panel selection
Select a diverse, capacity-matched set for Neuro.
ranked experimental package06 · Wet-lab validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Sequence–performance learning
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
NA-MRN · PRODUCT LINE
mRNAConstruct-AI
mRNA Construct Design
Designed to answer one actionable R&D question: which candidates, controls, and validation experiments should advance together?
DECISION WORKFLOW
Five evidence-preserving steps.
01
Lock the input, endpoint, controls, and exclusions
02
Generate a traceable candidate space
03
Calibrate orthogonal evidence and uncertainty
04
Select a diverse set under experimental constraints
05
Deliver assay-ready files and a data-return schema
CAT SOLUTIONS
4 defined solution packages.
NA-MRN-001Vaccine mRNA Construct
viral/tumor antigen ORF
240 constructs → 12 Pareto constructs →/Open solution →NA-MRN-002mRNA Construct
secreted therapeutic protein
216 constructs → 10 constructs →/Open solution →NA-MRN-003mRNA Construct
membrane antigen/receptor
192 constructs → 10 constructs → surfacesOpen solution →NA-MRN-004mRNA Construct
Cas9/base editor ORF
264 constructs → 12 constructs →/Open solution →AI Product Atlas
NA-MRN-001 · Illustrative planning example
Vaccine mRNA Construct
viral/tumor antigen ORF
Vaccine
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Sequence and modality specification
Define viral/tumor antigen ORF, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Candidate enumeration
Generate a traceable candidate space appropriate to mrna construct design.
candidate-space file and provenance03 · Efficacy and off-target calibration
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Chemistry and construct optimization
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Balanced panel selection
Select a diverse, capacity-matched set for Vaccine.
ranked experimental package06 · Wet-lab validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Sequence–performance learning
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
NA-MRN-002 · Illustrative planning example
mRNA Construct
secreted therapeutic protein
Nucleic Acid Therapeutics research and development
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Sequence and modality specification
Define secreted therapeutic protein, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Candidate enumeration
Generate a traceable candidate space appropriate to mrna construct design.
candidate-space file and provenance03 · Efficacy and off-target calibration
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Chemistry and construct optimization
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Balanced panel selection
Select a diverse, capacity-matched set for Nucleic Acid Therapeutics research and development.
ranked experimental package06 · Wet-lab validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Sequence–performance learning
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
NA-MRN-003 · Illustrative planning example
mRNA Construct
membrane antigen/receptor
cells
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Sequence and modality specification
Define membrane antigen/receptor, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Candidate enumeration
Generate a traceable candidate space appropriate to mrna construct design.
candidate-space file and provenance03 · Efficacy and off-target calibration
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Chemistry and construct optimization
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Balanced panel selection
Select a diverse, capacity-matched set for cells.
ranked experimental package06 · Wet-lab validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Sequence–performance learning
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
NA-MRN-004 · Illustrative planning example
mRNA Construct
Cas9/base editor ORF
Delivery
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Sequence and modality specification
Define Cas9/base editor ORF, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Candidate enumeration
Generate a traceable candidate space appropriate to mrna construct design.
candidate-space file and provenance03 · Efficacy and off-target calibration
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Chemistry and construct optimization
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Balanced panel selection
Select a diverse, capacity-matched set for Delivery.
ranked experimental package06 · Wet-lab validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Sequence–performance learning
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
NA-CIR · PRODUCT LINE
circRNAConstruct-AI
Circular RNA Construct Design
Designed to answer one actionable R&D question: which candidates, controls, and validation experiments should advance together?
DECISION WORKFLOW
Five evidence-preserving steps.
01
Lock the input, endpoint, controls, and exclusions
02
Generate a traceable candidate space
03
Calibrate orthogonal evidence and uncertainty
04
Select a diverse set under experimental constraints
05
Deliver assay-ready files and a data-return schema
CAT SOLUTIONS
4 defined solution packages.
NA-CIR-001Vaccine circRNA
vaccine antigen ORF
96 designs → 10 circRNA + 2 controlsOpen solution →NA-CIR-002cells circRNA
cytokine ORF
88 designs → 10 circRNA + 2 controlsOpen solution →NA-CIR-003circRNA
intracellular protein ORF
104 designs → 12 circRNA + 2 controlsOpen solution →NA-CIR-004circRNA
luciferase/GFP
72 designs → 8 circRNA + 4 controlsOpen solution →AI Product Atlas
NA-CIR-001 · Illustrative planning example
Vaccine circRNA
vaccine antigen ORF
Vaccine
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Sequence and modality specification
Define vaccine antigen ORF, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Candidate enumeration
Generate a traceable candidate space appropriate to circular rna construct design.
candidate-space file and provenance03 · Efficacy and off-target calibration
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Chemistry and construct optimization
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Balanced panel selection
Select a diverse, capacity-matched set for Vaccine.
ranked experimental package06 · Wet-lab validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Sequence–performance learning
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
NA-CIR-002 · Illustrative planning example
cells circRNA
cytokine ORF
Immunotherapy
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Sequence and modality specification
Define cytokine ORF, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Candidate enumeration
Generate a traceable candidate space appropriate to circular rna construct design.
candidate-space file and provenance03 · Efficacy and off-target calibration
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Chemistry and construct optimization
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Balanced panel selection
Select a diverse, capacity-matched set for Immunotherapy.
ranked experimental package06 · Wet-lab validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Sequence–performance learning
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
NA-CIR-003 · Illustrative planning example
circRNA
intracellular protein ORF
Nucleic Acid Therapeutics research and development
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Sequence and modality specification
Define intracellular protein ORF, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Candidate enumeration
Generate a traceable candidate space appropriate to circular rna construct design.
candidate-space file and provenance03 · Efficacy and off-target calibration
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Chemistry and construct optimization
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Balanced panel selection
Select a diverse, capacity-matched set for Nucleic Acid Therapeutics research and development.
ranked experimental package06 · Wet-lab validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Sequence–performance learning
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
NA-CIR-004 · Illustrative planning example
circRNA
luciferase/GFP
Nucleic Acid Therapeutics research and development
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Sequence and modality specification
Define luciferase/GFP, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Candidate enumeration
Generate a traceable candidate space appropriate to circular rna construct design.
candidate-space file and provenance03 · Efficacy and off-target calibration
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Chemistry and construct optimization
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Balanced panel selection
Select a diverse, capacity-matched set for Nucleic Acid Therapeutics research and development.
ranked experimental package06 · Wet-lab validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Sequence–performance learning
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
NA-APT · PRODUCT LINE
AptamerSeed-AI
Aptamer Seed-pool Design
Designed to answer one actionable R&D question: which candidates, controls, and validation experiments should advance together?
DECISION WORKFLOW
Five evidence-preserving steps.
01
Lock the input, endpoint, controls, and exclusions
02
Generate a traceable candidate space
03
Calibrate orthogonal evidence and uncertainty
04
Select a diverse set under experimental constraints
05
Deliver assay-ready files and a data-return schema
CAT SOLUTIONS
4 defined solution packages.
NA-APT-001PD-L1 Aptamer Seed Pool
PD-L1 extracellular domain
12,000 candidates → 480 structures seed → 12Open solution →NA-APT-002VEGF Aptamer Seed Pool
VEGF-A
10,000 candidates → 420 seed → 10Open solution →NA-APT-003Aptamer Seed Pool
thrombin exosites
8,000 candidates → 360 seed → 9Open solution →NA-APT-004EpCAM Aptamer Seed Pool
EpCAM extracellular domain
11,000 candidates → 450 seed → 11Open solution →AI Product Atlas
NA-APT-001 · Illustrative planning example
PD-L1 Aptamer Seed Pool
PD-L1 extracellular domain
Diagnostics
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Sequence and modality specification
Define PD-L1 extracellular domain, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Candidate enumeration
Generate a traceable candidate space appropriate to aptamer seed-pool design.
candidate-space file and provenance03 · Efficacy and off-target calibration
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Chemistry and construct optimization
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Balanced panel selection
Select a diverse, capacity-matched set for Diagnostics.
ranked experimental package06 · Wet-lab validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Sequence–performance learning
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
NA-APT-002 · Illustrative planning example
VEGF Aptamer Seed Pool
VEGF-A
Diagnostics
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Sequence and modality specification
Define VEGF-A, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Candidate enumeration
Generate a traceable candidate space appropriate to aptamer seed-pool design.
candidate-space file and provenance03 · Efficacy and off-target calibration
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Chemistry and construct optimization
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Balanced panel selection
Select a diverse, capacity-matched set for Diagnostics.
ranked experimental package06 · Wet-lab validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Sequence–performance learning
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
NA-APT-003 · Illustrative planning example
Aptamer Seed Pool
thrombin exosites
Sensor
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Sequence and modality specification
Define thrombin exosites, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Candidate enumeration
Generate a traceable candidate space appropriate to aptamer seed-pool design.
candidate-space file and provenance03 · Efficacy and off-target calibration
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Chemistry and construct optimization
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Balanced panel selection
Select a diverse, capacity-matched set for Sensor.
ranked experimental package06 · Wet-lab validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Sequence–performance learning
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
NA-APT-004 · Illustrative planning example
EpCAM Aptamer Seed Pool
EpCAM extracellular domain
Oncology Diagnostics
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Sequence and modality specification
Define EpCAM extracellular domain, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Candidate enumeration
Generate a traceable candidate space appropriate to aptamer seed-pool design.
candidate-space file and provenance03 · Efficacy and off-target calibration
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Chemistry and construct optimization
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Balanced panel selection
Select a diverse, capacity-matched set for Oncology Diagnostics.
ranked experimental package06 · Wet-lab validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Sequence–performance learning
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
IMG-PAT · PRODUCT LINE
IHCQuant-AI
IHC and Multiplex Pathology Quantification
Designed to answer one actionable R&D question: which candidates, controls, and validation experiments should advance together?
DECISION WORKFLOW
Five evidence-preserving steps.
01
Lock the input, endpoint, controls, and exclusions
02
Generate a traceable candidate space
03
Calibrate orthogonal evidence and uncertainty
04
Select a diverse set under experimental constraints
05
Deliver assay-ready files and a data-return schema
CAT SOLUTIONS
4 defined solution packages.
IMG-IHC-001Breast Cancer CD8 IHC Spatial Quantification
CD8+ cells × Oncology
120 WSI → 2.8M cells → 3 → 1Open solution →IMG-IHC-002PD-L1 TPS/CPS Computation and Review
PD-L1 IHC × Oncologycells/cells
180 WSI → 12 → cases TPS/CPS +Open solution →IMG-IHC-003HER2 Heterogeneity and Membrane-completeness Analysis
HER2 IHC × regions
96 WSI → 640 regions → 4 → 24Open solution →IMG-IHC-004Multiplex IHC Immune-neighborhood Analysis
CD8/CD4/FOXP3/CD68/PanCK
72 slides → 11 cells → 8 neighborhoods → 5 validation markerOpen solution →AI Product Atlas
IMG-IHC-001 · Illustrative planning example
Breast Cancer CD8 IHC Spatial Quantification
CD8+ cells × Oncology
and
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Endpoint and cohort definition
Define CD8+ cells × Oncology, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Image and label QC
Generate a traceable candidate space appropriate to ihc and multiplex pathology quantification.
candidate-space file and provenance03 · Segmentation, registration, or feature extraction
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Multicenter and uncertainty audit
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · ROI/model/marker selection
Select a diverse, capacity-matched set for and.
ranked experimental package06 · Pathology or assay validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Frozen external validation
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
IMG-IHC-002 · Illustrative planning example
PD-L1 TPS/CPS Computation and Review
PD-L1 IHC × Oncologycells/cells
Immunotherapy
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Endpoint and cohort definition
Define PD-L1 IHC × Oncologycells/cells, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Image and label QC
Generate a traceable candidate space appropriate to ihc and multiplex pathology quantification.
candidate-space file and provenance03 · Segmentation, registration, or feature extraction
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Multicenter and uncertainty audit
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · ROI/model/marker selection
Select a diverse, capacity-matched set for Immunotherapy.
ranked experimental package06 · Pathology or assay validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Frozen external validation
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
IMG-IHC-003 · Illustrative planning example
HER2 Heterogeneity and Membrane-completeness Analysis
HER2 IHC × regions
Bioimaging & Spatial Biology research and development
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Endpoint and cohort definition
Define HER2 IHC × regions, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Image and label QC
Generate a traceable candidate space appropriate to ihc and multiplex pathology quantification.
candidate-space file and provenance03 · Segmentation, registration, or feature extraction
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Multicenter and uncertainty audit
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · ROI/model/marker selection
Select a diverse, capacity-matched set for Bioimaging & Spatial Biology research and development.
ranked experimental package06 · Pathology or assay validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Frozen external validation
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
IMG-IHC-004 · Illustrative planning example
Multiplex IHC Immune-neighborhood Analysis
CD8/CD4/FOXP3/CD68/PanCK
Oncology
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Endpoint and cohort definition
Define CD8/CD4/FOXP3/CD68/PanCK, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Image and label QC
Generate a traceable candidate space appropriate to ihc and multiplex pathology quantification.
candidate-space file and provenance03 · Segmentation, registration, or feature extraction
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Multicenter and uncertainty audit
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · ROI/model/marker selection
Select a diverse, capacity-matched set for Oncology.
ranked experimental package06 · Pathology or assay validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Frozen external validation
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
IMG-SPX · PRODUCT LINE
SpatialMap-AI
Spatial Transcriptomic States and Niches
Designed to answer one actionable R&D question: which candidates, controls, and validation experiments should advance together?
DECISION WORKFLOW
Five evidence-preserving steps.
01
Lock the input, endpoint, controls, and exclusions
02
Generate a traceable candidate space
03
Calibrate orthogonal evidence and uncertainty
04
Select a diverse set under experimental constraints
05
Deliver assay-ready files and a data-return schema
CAT SOLUTIONS
4 defined solution packages.
IMG-SPX-001Colorectal Cancer Immune–Tumor Boundary Atlas
CRC tumor/adjacent Visium + scRNA reference
18 slides → 14 states → 6 → 10-marker validationOpen solution →IMG-SPX-002Breast Cancer Immune-exclusion Niche
breast tumor spatial RNA + H&E + IHC
24 slides → 9 spatial domains → 4 → 8-marker panelOpen solution →IMG-SPX-003Liver Fibrosis Spatial Zonation
fibrotic liver zonation + stellate/macrophage states
20 slides → 12 states → 5 neighborhoods → 6Open solution →IMG-SPX-004Neuroinflammatory Cell Neighborhoods
brain spatial transcriptomics + glial reference
16 regions → 10 states → 4 lesionsneighborhoods → 8 validation markerOpen solution →AI Product Atlas
IMG-SPX-001 · Illustrative planning example
Colorectal Cancer Immune–Tumor Boundary Atlas
CRC tumor/adjacent Visium + scRNA reference
Colorectal Cancer
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Endpoint and cohort definition
Define CRC tumor/adjacent Visium + scRNA reference, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Image and label QC
Generate a traceable candidate space appropriate to spatial transcriptomic states and niches.
candidate-space file and provenance03 · Segmentation, registration, or feature extraction
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Multicenter and uncertainty audit
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · ROI/model/marker selection
Select a diverse, capacity-matched set for Colorectal Cancer.
ranked experimental package06 · Pathology or assay validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Frozen external validation
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
IMG-SPX-002 · Illustrative planning example
Breast Cancer Immune-exclusion Niche
breast tumor spatial RNA + H&E + IHC
ICI
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Endpoint and cohort definition
Define breast tumor spatial RNA + H&E + IHC, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Image and label QC
Generate a traceable candidate space appropriate to spatial transcriptomic states and niches.
candidate-space file and provenance03 · Segmentation, registration, or feature extraction
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Multicenter and uncertainty audit
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · ROI/model/marker selection
Select a diverse, capacity-matched set for ICI.
ranked experimental package06 · Pathology or assay validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Frozen external validation
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
IMG-SPX-003 · Illustrative planning example
Liver Fibrosis Spatial Zonation
fibrotic liver zonation + stellate/macrophage states
Bioimaging & Spatial Biology research and development
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Endpoint and cohort definition
Define fibrotic liver zonation + stellate/macrophage states, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Image and label QC
Generate a traceable candidate space appropriate to spatial transcriptomic states and niches.
candidate-space file and provenance03 · Segmentation, registration, or feature extraction
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Multicenter and uncertainty audit
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · ROI/model/marker selection
Select a diverse, capacity-matched set for Bioimaging & Spatial Biology research and development.
ranked experimental package06 · Pathology or assay validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Frozen external validation
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
IMG-SPX-004 · Illustrative planning example
Neuroinflammatory Cell Neighborhoods
brain spatial transcriptomics + glial reference
Neuro and
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Endpoint and cohort definition
Define brain spatial transcriptomics + glial reference, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Image and label QC
Generate a traceable candidate space appropriate to spatial transcriptomic states and niches.
candidate-space file and provenance03 · Segmentation, registration, or feature extraction
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Multicenter and uncertainty audit
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · ROI/model/marker selection
Select a diverse, capacity-matched set for Neuro and.
ranked experimental package06 · Pathology or assay validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Frozen external validation
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
IMG-HST · PRODUCT LINE
HistoFeature-AI
H&E Morphology and Weakly Supervised Models
Designed to answer one actionable R&D question: which candidates, controls, and validation experiments should advance together?
DECISION WORKFLOW
Five evidence-preserving steps.
01
Lock the input, endpoint, controls, and exclusions
02
Generate a traceable candidate space
03
Calibrate orthogonal evidence and uncertainty
04
Select a diverse set under experimental constraints
05
Deliver assay-ready files and a data-return schema
CAT SOLUTIONS
4 defined solution packages.
IMG-HST-001H&E Immune-hot/Cold Phenotype
breast/NSCLC H&E + immune labels
480 WSI → 42 candidates → 6 robust features → 1 modelsOpen solution →IMG-HST-002Research-use Colorectal MSI Prescreen
CRC H&E + MSI status
1,200 WSI → → ROI +Open solution →IMG-HST-003Digital Morphology Score for Liver Fibrosis
liver biopsy H&E/trichrome
320 WSI → 7 structures → +Open solution →IMG-HST-004Tumor Necrosis and Treatment-effect Quantification
resection H&E pre/post therapy
210 WSI → Oncology//→ casesOpen solution →AI Product Atlas
IMG-HST-001 · Illustrative planning example
H&E Immune-hot/Cold Phenotype
breast/NSCLC H&E + immune labels
Immunotherapy
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Endpoint and cohort definition
Define breast/NSCLC H&E + immune labels, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Image and label QC
Generate a traceable candidate space appropriate to h&e morphology and weakly supervised models.
candidate-space file and provenance03 · Segmentation, registration, or feature extraction
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Multicenter and uncertainty audit
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · ROI/model/marker selection
Select a diverse, capacity-matched set for Immunotherapy.
ranked experimental package06 · Pathology or assay validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Frozen external validation
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
IMG-HST-002 · Illustrative planning example
Research-use Colorectal MSI Prescreen
CRC H&E + MSI status
Bioimaging & Spatial Biology research and development
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Endpoint and cohort definition
Define CRC H&E + MSI status, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Image and label QC
Generate a traceable candidate space appropriate to h&e morphology and weakly supervised models.
candidate-space file and provenance03 · Segmentation, registration, or feature extraction
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Multicenter and uncertainty audit
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · ROI/model/marker selection
Select a diverse, capacity-matched set for Bioimaging & Spatial Biology research and development.
ranked experimental package06 · Pathology or assay validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Frozen external validation
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
IMG-HST-003 · Illustrative planning example
Digital Morphology Score for Liver Fibrosis
liver biopsy H&E/trichrome
NASH
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Endpoint and cohort definition
Define liver biopsy H&E/trichrome, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Image and label QC
Generate a traceable candidate space appropriate to h&e morphology and weakly supervised models.
candidate-space file and provenance03 · Segmentation, registration, or feature extraction
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Multicenter and uncertainty audit
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · ROI/model/marker selection
Select a diverse, capacity-matched set for NASH.
ranked experimental package06 · Pathology or assay validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Frozen external validation
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
IMG-HST-004 · Illustrative planning example
Tumor Necrosis and Treatment-effect Quantification
resection H&E pre/post therapy
Bioimaging & Spatial Biology research and development
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Endpoint and cohort definition
Define resection H&E pre/post therapy, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Image and label QC
Generate a traceable candidate space appropriate to h&e morphology and weakly supervised models.
candidate-space file and provenance03 · Segmentation, registration, or feature extraction
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Multicenter and uncertainty audit
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · ROI/model/marker selection
Select a diverse, capacity-matched set for Bioimaging & Spatial Biology research and development.
ranked experimental package06 · Pathology or assay validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Frozen external validation
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
IMG-RAD · PRODUCT LINE
RadioPath-AI
Imaging-to-Microenvironment Prediction
Designed to answer one actionable R&D question: which candidates, controls, and validation experiments should advance together?
DECISION WORKFLOW
Five evidence-preserving steps.
01
Lock the input, endpoint, controls, and exclusions
02
Generate a traceable candidate space
03
Calibrate orthogonal evidence and uncertainty
04
Select a diverse set under experimental constraints
05
Deliver assay-ready files and a data-return schema
CAT SOLUTIONS
4 defined solution packages.
IMG-RAD-001Breast Cancer MRI–CD8+ TIL Spatial Prediction
DCE-MRI × CD8 IHC/TIL/RNA-seq
182 → 91 → AUC 0.567/0.713 → validationOpen solution →IMG-RAD-002NSCLC CT/
CT × PD-L1/CD8/RNA immune signature
620 → 84 robust features → 1 modelsOpen solution →IMG-RAD-003MRI and
multiparametric MRI × fibrosis/spatial transcriptomics
240 → 5 → + ROIOpen solution →IMG-RAD-004MRI and
multisequence MRI × IDH/immune microenvironment
410 → QC →/models +Open solution →AI Product Atlas
IMG-RAD-001 · Real-data feasibility and model-audit case
Breast Cancer MRI–CD8+ TIL Spatial Prediction
DCE-MRI × CD8 IHC/TIL/RNA-seq
Breast Cancer
Breast MRI prediction of CD8+ TILs
The published 182-case DCE-MRI design was audited and then rerun on 91 real multicenter MRI cases with expert segmentations and paired RNA evidence. The workflow was executable, but the locked-test evidence did not support clinical-grade prediction.
91real MRI + expert segmentation cases
0.567locked-test AUC for the original label
0.713best locked AUC for a geometric phenotype
0.742multiplicity-adjusted gate
ρ=0.390TIL label vs CD8 RNA signature
What worked
DICOM ingestion, segmentation audit, feature lineage, leakage-free train/test evaluation, and RNA label checks.
What failed
AUC 0.567 with wide uncertainty and permutation p=0.18 did not clear the predeclared evidence gate.
Next step
Prioritize trusted labels, larger center-diverse cohorts, and frozen external validation before adding model complexity.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Endpoint and cohort definition
Define DCE-MRI × CD8 IHC/TIL/RNA-seq, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Image and label QC
Generate a traceable candidate space appropriate to imaging-to-microenvironment prediction.
candidate-space file and provenance03 · Segmentation, registration, or feature extraction
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Multicenter and uncertainty audit
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · ROI/model/marker selection
Select a diverse, capacity-matched set for Breast Cancer.
ranked experimental package06 · Pathology or assay validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Frozen external validation
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
IMG-RAD-002 · Illustrative planning example
NSCLC CT/
CT × PD-L1/CD8/RNA immune signature
Immunotherapy
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Endpoint and cohort definition
Define CT × PD-L1/CD8/RNA immune signature, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Image and label QC
Generate a traceable candidate space appropriate to imaging-to-microenvironment prediction.
candidate-space file and provenance03 · Segmentation, registration, or feature extraction
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Multicenter and uncertainty audit
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · ROI/model/marker selection
Select a diverse, capacity-matched set for Immunotherapy.
ranked experimental package06 · Pathology or assay validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Frozen external validation
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
IMG-RAD-003 · Illustrative planning example
MRI and
multiparametric MRI × fibrosis/spatial transcriptomics
Drug
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Endpoint and cohort definition
Define multiparametric MRI × fibrosis/spatial transcriptomics, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Image and label QC
Generate a traceable candidate space appropriate to imaging-to-microenvironment prediction.
candidate-space file and provenance03 · Segmentation, registration, or feature extraction
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Multicenter and uncertainty audit
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · ROI/model/marker selection
Select a diverse, capacity-matched set for Drug.
ranked experimental package06 · Pathology or assay validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Frozen external validation
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
IMG-RAD-004 · Illustrative planning example
MRI and
multisequence MRI × IDH/immune microenvironment
Neuro Oncology
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Endpoint and cohort definition
Define multisequence MRI × IDH/immune microenvironment, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Image and label QC
Generate a traceable candidate space appropriate to imaging-to-microenvironment prediction.
candidate-space file and provenance03 · Segmentation, registration, or feature extraction
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Multicenter and uncertainty audit
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · ROI/model/marker selection
Select a diverse, capacity-matched set for Neuro Oncology.
ranked experimental package06 · Pathology or assay validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Frozen external validation
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
IMG-RSP · PRODUCT LINE
ResponseImage-AI
Longitudinal Imaging and Treatment Response
Designed to answer one actionable R&D question: which candidates, controls, and validation experiments should advance together?
DECISION WORKFLOW
Five evidence-preserving steps.
01
Lock the input, endpoint, controls, and exclusions
02
Generate a traceable candidate space
03
Calibrate orthogonal evidence and uncertainty
04
Select a diverse set under experimental constraints
05
Deliver assay-ready files and a data-return schema
CAT SOLUTIONS
4 defined solution packages.
IMG-RSP-001Breast Cancer Neoadjuvant DCE-MRI Early Response
baseline/early-treatment DCE-MRI × pCR/RCB
260 → 3 → 8 delta → riskmodelsOpen solution →IMG-RSP-002ICI Lesion-level Response and Pseudoprogression
serial CT × lesion response × immune outcomes
380 patients/1,420 lesions → 5 → patientsOpen solution →IMG-RSP-003ADC Treatment-response Imaging Heterogeneity
serial CT/MRI × lesion-level response
210 patients → lesions vs. → 4 → casesOpen solution →IMG-RSP-004Drug Longitudinal Tissue Imaging
paired biopsy/SHG/MRI
160 vs.samples → 6 →Open solution →AI Product Atlas
IMG-RSP-001 · Illustrative planning example
Breast Cancer Neoadjuvant DCE-MRI Early Response
baseline/early-treatment DCE-MRI × pCR/RCB
Bioimaging & Spatial Biology research and development
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Endpoint and cohort definition
Define baseline/early-treatment DCE-MRI × pCR/RCB, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Image and label QC
Generate a traceable candidate space appropriate to longitudinal imaging and treatment response.
candidate-space file and provenance03 · Segmentation, registration, or feature extraction
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Multicenter and uncertainty audit
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · ROI/model/marker selection
Select a diverse, capacity-matched set for Bioimaging & Spatial Biology research and development.
ranked experimental package06 · Pathology or assay validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Frozen external validation
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
IMG-RSP-002 · Illustrative planning example
ICI Lesion-level Response and Pseudoprogression
serial CT × lesion response × immune outcomes
Immunotherapy
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Endpoint and cohort definition
Define serial CT × lesion response × immune outcomes, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Image and label QC
Generate a traceable candidate space appropriate to longitudinal imaging and treatment response.
candidate-space file and provenance03 · Segmentation, registration, or feature extraction
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Multicenter and uncertainty audit
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · ROI/model/marker selection
Select a diverse, capacity-matched set for Immunotherapy.
ranked experimental package06 · Pathology or assay validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Frozen external validation
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
IMG-RSP-003 · Illustrative planning example
ADC Treatment-response Imaging Heterogeneity
serial CT/MRI × lesion-level response
ADC
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Endpoint and cohort definition
Define serial CT/MRI × lesion-level response, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Image and label QC
Generate a traceable candidate space appropriate to longitudinal imaging and treatment response.
candidate-space file and provenance03 · Segmentation, registration, or feature extraction
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Multicenter and uncertainty audit
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · ROI/model/marker selection
Select a diverse, capacity-matched set for ADC.
ranked experimental package06 · Pathology or assay validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Frozen external validation
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
IMG-RSP-004 · Illustrative planning example
Drug Longitudinal Tissue Imaging
paired biopsy/SHG/MRI
Bioimaging & Spatial Biology research and development
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Endpoint and cohort definition
Define paired biopsy/SHG/MRI, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Image and label QC
Generate a traceable candidate space appropriate to longitudinal imaging and treatment response.
candidate-space file and provenance03 · Segmentation, registration, or feature extraction
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Multicenter and uncertainty audit
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · ROI/model/marker selection
Select a diverse, capacity-matched set for Bioimaging & Spatial Biology research and development.
ranked experimental package06 · Pathology or assay validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Frozen external validation
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
IMG-REG · PRODUCT LINE
SpatialBridge-AI
Cross-modal Registration and Label Transfer
Designed to answer one actionable R&D question: which candidates, controls, and validation experiments should advance together?
DECISION WORKFLOW
Five evidence-preserving steps.
01
Lock the input, endpoint, controls, and exclusions
02
Generate a traceable candidate space
03
Calibrate orthogonal evidence and uncertainty
04
Select a diverse set under experimental constraints
05
Deliver assay-ready files and a data-return schema
CAT SOLUTIONS
4 defined solution packages.
IMG-REG-001H&E–IHC– Trimodal Registration
serial sections/same-slide multimodal tissue
12 samples → 3 → → ROIOpen solution →IMG-REG-002MRI– slides Spatial Bridging
resection specimen × ex vivo/in vivo MRI
36 → → – → regionsOpen solution →IMG-REG-003cells Reference Mapping
IMC/CODEX × scRNA reference
2.4M cells → 16 states → neighborhoods + OODOpen solution →IMG-REG-004Drug regions Cross-modal Validation
spatial RNA × phospho-IHC × morphology
18 slides → regions → 6 ROI → validation panelOpen solution →AI Product Atlas
IMG-REG-001 · Illustrative planning example
H&E–IHC– Trimodal Registration
serial sections/same-slide multimodal tissue
Bioimaging & Spatial Biology research and development
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Endpoint and cohort definition
Define serial sections/same-slide multimodal tissue, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Image and label QC
Generate a traceable candidate space appropriate to cross-modal registration and label transfer.
candidate-space file and provenance03 · Segmentation, registration, or feature extraction
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Multicenter and uncertainty audit
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · ROI/model/marker selection
Select a diverse, capacity-matched set for Bioimaging & Spatial Biology research and development.
ranked experimental package06 · Pathology or assay validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Frozen external validation
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
IMG-REG-002 · Illustrative planning example
MRI– slides Spatial Bridging
resection specimen × ex vivo/in vivo MRI
Bioimaging & Spatial Biology research and development
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Endpoint and cohort definition
Define resection specimen × ex vivo/in vivo MRI, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Image and label QC
Generate a traceable candidate space appropriate to cross-modal registration and label transfer.
candidate-space file and provenance03 · Segmentation, registration, or feature extraction
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Multicenter and uncertainty audit
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · ROI/model/marker selection
Select a diverse, capacity-matched set for Bioimaging & Spatial Biology research and development.
ranked experimental package06 · Pathology or assay validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Frozen external validation
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
IMG-REG-003 · Illustrative planning example
cells Reference Mapping
IMC/CODEX × scRNA reference
Bioimaging & Spatial Biology research and development
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Endpoint and cohort definition
Define IMC/CODEX × scRNA reference, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Image and label QC
Generate a traceable candidate space appropriate to cross-modal registration and label transfer.
candidate-space file and provenance03 · Segmentation, registration, or feature extraction
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Multicenter and uncertainty audit
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · ROI/model/marker selection
Select a diverse, capacity-matched set for Bioimaging & Spatial Biology research and development.
ranked experimental package06 · Pathology or assay validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Frozen external validation
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
IMG-REG-004 · Illustrative planning example
Drug regions Cross-modal Validation
spatial RNA × phospho-IHC × morphology
Bioimaging & Spatial Biology research and development
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Endpoint and cohort definition
Define spatial RNA × phospho-IHC × morphology, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Image and label QC
Generate a traceable candidate space appropriate to cross-modal registration and label transfer.
candidate-space file and provenance03 · Segmentation, registration, or feature extraction
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · Multicenter and uncertainty audit
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · ROI/model/marker selection
Select a diverse, capacity-matched set for Bioimaging & Spatial Biology research and development.
ranked experimental package06 · Pathology or assay validation
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Frozen external validation
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
MAT-CAS · PRODUCT LINE
CatScreen-AI
Catalyst Adsorption Screening
Designed to answer one actionable R&D question: which candidates, controls, and validation experiments should advance together?
DECISION WORKFLOW
Five evidence-preserving steps.
01
Lock the input, endpoint, controls, and exclusions
02
Generate a traceable candidate space
03
Calibrate orthogonal evidence and uncertainty
04
Select a diverse set under experimental constraints
05
Deliver assay-ready files and a data-return schema
CAT SOLUTIONS
2 defined solution packages.
AI Product Atlas
CAT-ADS-001 · Illustrative planning example
Transition-metal/Alloy *OH·*O·*OOH Adsorption Scaling
candidates +
OER/ORR Catalysis
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Material objective and reference states
Define candidates +, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Composition/structure enumeration
Generate a traceable candidate space appropriate to catalyst adsorption screening.
candidate-space file and provenance03 · Relaxation and descriptor calculation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · DFT/experimental calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Constrained candidate selection
Select a diverse, capacity-matched set for OER/ORR Catalysis.
ranked experimental package06 · Synthesis and characterization
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Closed-loop materials update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
CAT-ADS-002 · Illustrative planning example
CO₂RR *CO/*COOH Selectivity
sites
AI-guided Materials R&D research and development
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Material objective and reference states
Define sites, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Composition/structure enumeration
Generate a traceable candidate space appropriate to catalyst adsorption screening.
candidate-space file and provenance03 · Relaxation and descriptor calculation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · DFT/experimental calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Constrained candidate selection
Select a diverse, capacity-matched set for AI-guided Materials R&D research and development.
ranked experimental package06 · Synthesis and characterization
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Closed-loop materials update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
MAT-VOL · PRODUCT LINE
VolcanoMap-AI
Volcano Maps and Reaction Barriers
Designed to answer one actionable R&D question: which candidates, controls, and validation experiments should advance together?
DECISION WORKFLOW
Five evidence-preserving steps.
01
Lock the input, endpoint, controls, and exclusions
02
Generate a traceable candidate space
03
Calibrate orthogonal evidence and uncertainty
04
Select a diverse set under experimental constraints
05
Deliver assay-ready files and a data-return schema
CAT SOLUTIONS
2 defined solution packages.
AI Product Atlas
CAT-VOL-001 · Illustrative planning example
HER ΔG_H* Volcano Screening
Project-defined target and comparator set
Catalysis
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Material objective and reference states
Define Project-defined target and comparator set, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Composition/structure enumeration
Generate a traceable candidate space appropriate to volcano maps and reaction barriers.
candidate-space file and provenance03 · Relaxation and descriptor calculation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · DFT/experimental calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Constrained candidate selection
Select a diverse, capacity-matched set for Catalysis.
ranked experimental package06 · Synthesis and characterization
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Closed-loop materials update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
CAT-VOL-002 · Illustrative planning example
OER Four-electron Pathway Rate-limiting Step
surfaces
Catalysis
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Material objective and reference states
Define surfaces, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Composition/structure enumeration
Generate a traceable candidate space appropriate to volcano maps and reaction barriers.
candidate-space file and provenance03 · Relaxation and descriptor calculation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · DFT/experimental calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Constrained candidate selection
Select a diverse, capacity-matched set for Catalysis.
ranked experimental package06 · Synthesis and characterization
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Closed-loop materials update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
MAT-SAC · PRODUCT LINE
SAC-AI
Single-atom and Doped Catalysts
Designed to answer one actionable R&D question: which candidates, controls, and validation experiments should advance together?
DECISION WORKFLOW
Five evidence-preserving steps.
01
Lock the input, endpoint, controls, and exclusions
02
Generate a traceable candidate space
03
Calibrate orthogonal evidence and uncertainty
04
Select a diverse set under experimental constraints
05
Deliver assay-ready files and a data-return schema
CAT SOLUTIONS
1 defined solution packages.
AI Product Atlas
CAT-SAC-001 · Illustrative planning example
N-doped Carbon-supported Single-atom ORR Activity
27 ×
ORR Catalysis
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Material objective and reference states
Define 27 ×, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Composition/structure enumeration
Generate a traceable candidate space appropriate to single-atom and doped catalysts.
candidate-space file and provenance03 · Relaxation and descriptor calculation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · DFT/experimental calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Constrained candidate selection
Select a diverse, capacity-matched set for ORR Catalysis.
ranked experimental package06 · Synthesis and characterization
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Closed-loop materials update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
MAT-BAV · PRODUCT LINE
BatVolt-AI
Electrode Intercalation Voltage
Designed to answer one actionable R&D question: which candidates, controls, and validation experiments should advance together?
DECISION WORKFLOW
Five evidence-preserving steps.
01
Lock the input, endpoint, controls, and exclusions
02
Generate a traceable candidate space
03
Calibrate orthogonal evidence and uncertainty
04
Select a diverse set under experimental constraints
05
Deliver assay-ready files and a data-return schema
CAT SOLUTIONS
1 defined solution packages.
AI Product Atlas
ENE-BAT-001 · Illustrative planning example
Average Li Intercalation Voltage of Layered-oxide Cathodes
Project-defined target and comparator set
AI-guided Materials R&D research and development
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Material objective and reference states
Define Project-defined target and comparator set, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Composition/structure enumeration
Generate a traceable candidate space appropriate to electrode intercalation voltage.
candidate-space file and provenance03 · Relaxation and descriptor calculation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · DFT/experimental calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Constrained candidate selection
Select a diverse, capacity-matched set for AI-guided Materials R&D research and development.
ranked experimental package06 · Synthesis and characterization
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Closed-loop materials update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
MAT-ELY · PRODUCT LINE
Electrolyte-AI
Electrolyte Stability Window and Solvation
Designed to answer one actionable R&D question: which candidates, controls, and validation experiments should advance together?
DECISION WORKFLOW
Five evidence-preserving steps.
01
Lock the input, endpoint, controls, and exclusions
02
Generate a traceable candidate space
03
Calibrate orthogonal evidence and uncertainty
04
Select a diverse set under experimental constraints
05
Deliver assay-ready files and a data-return schema
CAT SOLUTIONS
1 defined solution packages.
AI Product Atlas
ENE-ELY-001 · Illustrative planning example
Solvent/Salt HOMO–LUMO Stability Window
× designs
Liquid Electrolyte
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Material objective and reference states
Define × designs, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Composition/structure enumeration
Generate a traceable candidate space appropriate to electrolyte stability window and solvation.
candidate-space file and provenance03 · Relaxation and descriptor calculation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · DFT/experimental calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Constrained candidate selection
Select a diverse, capacity-matched set for Liquid Electrolyte.
ranked experimental package06 · Synthesis and characterization
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Closed-loop materials update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
MAT-SIO · PRODUCT LINE
SolidION-AI
Solid-electrolyte Ion Migration
Designed to answer one actionable R&D question: which candidates, controls, and validation experiments should advance together?
DECISION WORKFLOW
Five evidence-preserving steps.
01
Lock the input, endpoint, controls, and exclusions
02
Generate a traceable candidate space
03
Calibrate orthogonal evidence and uncertainty
04
Select a diverse set under experimental constraints
05
Deliver assay-ready files and a data-return schema
CAT SOLUTIONS
1 defined solution packages.
AI Product Atlas
ENE-SSE-001 · Illustrative planning example
/Li+ Migration Barrier
candidatesstructures +
Solid-state Battery
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Material objective and reference states
Define candidatesstructures +, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Composition/structure enumeration
Generate a traceable candidate space appropriate to solid-electrolyte ion migration.
candidate-space file and provenance03 · Relaxation and descriptor calculation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · DFT/experimental calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Constrained candidate selection
Select a diverse, capacity-matched set for Solid-state Battery.
ranked experimental package06 · Synthesis and characterization
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Closed-loop materials update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
MAT-RDX · PRODUCT LINE
RedoxFinger-AI
Redox Potentials and PCET
Designed to answer one actionable R&D question: which candidates, controls, and validation experiments should advance together?
DECISION WORKFLOW
Five evidence-preserving steps.
01
Lock the input, endpoint, controls, and exclusions
02
Generate a traceable candidate space
03
Calibrate orthogonal evidence and uncertainty
04
Select a diverse set under experimental constraints
05
Deliver assay-ready files and a data-return schema
CAT SOLUTIONS
1 defined solution packages.
AI Product Atlas
ECH-RDX-001 · Reproduced computational demonstration
Drug/1e− and PCET Oxidation Potential
SMILES + pH +
Drugand
Drug/1e− and PCET Oxidation Potential
This demonstration uses reproducible structure relaxation and electronic-structure descriptors to establish a relative ranking and a testable mechanism. Absolute energies or affinities are not presented as experimental truth.
Computational role
Converge candidate structures, identify stable configurations, and expose the descriptors that drive the ranking.
Decision output
3 → FMO/IP → PCET(pH 7) →
Evidence boundary
Final performance requires project-specific synthesis, electrochemical, surface, binding, or release validation.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Material objective and reference states
Define SMILES + pH +, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Composition/structure enumeration
Generate a traceable candidate space appropriate to redox potentials and pcet.
candidate-space file and provenance03 · Relaxation and descriptor calculation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · DFT/experimental calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Constrained candidate selection
Select a diverse, capacity-matched set for Drugand.
ranked experimental package06 · Synthesis and characterization
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Closed-loop materials update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
MAT-SAR · PRODUCT LINE
SensorArray-AI
Recognition Materials and Array Deconvolution
Designed to answer one actionable R&D question: which candidates, controls, and validation experiments should advance together?
DECISION WORKFLOW
Five evidence-preserving steps.
01
Lock the input, endpoint, controls, and exclusions
02
Generate a traceable candidate space
03
Calibrate orthogonal evidence and uncertainty
04
Select a diverse set under experimental constraints
05
Deliver assay-ready files and a data-return schema
CAT SOLUTIONS
1 defined solution packages.
AI Product Atlas
ECH-ARR-001 · Illustrative planning example
Electrode/Recognition-layer Selection and Cross-response
Materials ×
AI-guided Materials R&D research and development
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Material objective and reference states
Define Materials ×, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Composition/structure enumeration
Generate a traceable candidate space appropriate to recognition materials and array deconvolution.
candidate-space file and provenance03 · Relaxation and descriptor calculation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · DFT/experimental calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Constrained candidate selection
Select a diverse, capacity-matched set for AI-guided Materials R&D research and development.
ranked experimental package06 · Synthesis and characterization
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Closed-loop materials update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
MAT-INF · PRODUCT LINE
Interferent-AI
Interferent Separation Design
Designed to answer one actionable R&D question: which candidates, controls, and validation experiments should advance together?
DECISION WORKFLOW
Five evidence-preserving steps.
01
Lock the input, endpoint, controls, and exclusions
02
Generate a traceable candidate space
03
Calibrate orthogonal evidence and uncertainty
04
Select a diverse set under experimental constraints
05
Deliver assay-ready files and a data-return schema
CAT SOLUTIONS
1 defined solution packages.
AI Product Atlas
ECH-INT-001 · Reproduced computational demonstration
DA/AA/UA Three-analyte Potential Separation
Project-defined target and comparator set
Neuro and
DA/AA/UA Three-analyte Potential Separation
This demonstration uses reproducible structure relaxation and electronic-structure descriptors to establish a relative ranking and a testable mechanism. Absolute energies or affinities are not presented as experimental truth.
Computational role
Converge candidate structures, identify stable configurations, and expose the descriptors that drive the ranking.
Decision output
3 → PCET → → electrode-modification recommendation
Evidence boundary
Final performance requires project-specific synthesis, electrochemical, surface, binding, or release validation.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Material objective and reference states
Define Project-defined target and comparator set, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Composition/structure enumeration
Generate a traceable candidate space appropriate to interferent separation design.
candidate-space file and provenance03 · Relaxation and descriptor calculation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · DFT/experimental calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Constrained candidate selection
Select a diverse, capacity-matched set for Neuro and.
ranked experimental package06 · Synthesis and characterization
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Closed-loop materials update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
MAT-MPR · PRODUCT LINE
MaterialParam-AI
Materials and Interface Force-field Parameters
Designed to answer one actionable R&D question: which candidates, controls, and validation experiments should advance together?
DECISION WORKFLOW
Five evidence-preserving steps.
01
Lock the input, endpoint, controls, and exclusions
02
Generate a traceable candidate space
03
Calibrate orthogonal evidence and uncertainty
04
Select a diverse set under experimental constraints
05
Deliver assay-ready files and a data-return schema
CAT SOLUTIONS
3 defined solution packages.
FF-PAR-001Organometallic/Metal-center Parameter Package
Project-defined target and comparator set
N → QM →/→ multi-engine formatsOpen solution →FF-PAR-002Covalent-warhead Adduct Parameter Package
Project-defined target and comparator set
12 → → → 5Open solution →FF-PAR-003Ligand/Surface Adsorption Force Field
+ surfaces
configurations → → parameterization → MDOpen solution →AI Product Atlas
FF-PAR-001 · Illustrative planning example
Organometallic/Metal-center Parameter Package
Project-defined target and comparator set
and
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Material objective and reference states
Define Project-defined target and comparator set, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Composition/structure enumeration
Generate a traceable candidate space appropriate to materials and interface force-field parameters.
candidate-space file and provenance03 · Relaxation and descriptor calculation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · DFT/experimental calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Constrained candidate selection
Select a diverse, capacity-matched set for and.
ranked experimental package06 · Synthesis and characterization
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Closed-loop materials update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
FF-PAR-002 · Illustrative planning example
Covalent-warhead Adduct Parameter Package
Project-defined target and comparator set
AI-guided Materials R&D research and development
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Material objective and reference states
Define Project-defined target and comparator set, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Composition/structure enumeration
Generate a traceable candidate space appropriate to materials and interface force-field parameters.
candidate-space file and provenance03 · Relaxation and descriptor calculation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · DFT/experimental calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Constrained candidate selection
Select a diverse, capacity-matched set for AI-guided Materials R&D research and development.
ranked experimental package06 · Synthesis and characterization
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Closed-loop materials update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
FF-PAR-003 · Illustrative planning example
Ligand/Surface Adsorption Force Field
+ surfaces
and
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Material objective and reference states
Define + surfaces, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Composition/structure enumeration
Generate a traceable candidate space appropriate to materials and interface force-field parameters.
candidate-space file and provenance03 · Relaxation and descriptor calculation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · DFT/experimental calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Constrained candidate selection
Select a diverse, capacity-matched set for and.
ranked experimental package06 · Synthesis and characterization
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Closed-loop materials update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
MAT-TOR · PRODUCT LINE
TorsionQM-AI
Torsion and Conformer Reference Data
Designed to answer one actionable R&D question: which candidates, controls, and validation experiments should advance together?
DECISION WORKFLOW
Five evidence-preserving steps.
01
Lock the input, endpoint, controls, and exclusions
02
Generate a traceable candidate space
03
Calibrate orthogonal evidence and uncertainty
04
Select a diverse set under experimental constraints
05
Deliver assay-ready files and a data-return schema
CAT SOLUTIONS
1 defined solution packages.
AI Product Atlas
FF-TOR-001 · Illustrative planning example
Drug-molecule Torsional Energy Surface
Project-defined target and comparator set
parameterization
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Material objective and reference states
Define Project-defined target and comparator set, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Composition/structure enumeration
Generate a traceable candidate space appropriate to torsion and conformer reference data.
candidate-space file and provenance03 · Relaxation and descriptor calculation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · DFT/experimental calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Constrained candidate selection
Select a diverse, capacity-matched set for parameterization.
ranked experimental package06 · Synthesis and characterization
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Closed-loop materials update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
MAT-COR · PRODUCT LINE
CorrosionGuard-AI
Corrosion-inhibitor Adsorption Screening
Designed to answer one actionable R&D question: which candidates, controls, and validation experiments should advance together?
DECISION WORKFLOW
Five evidence-preserving steps.
01
Lock the input, endpoint, controls, and exclusions
02
Generate a traceable candidate space
03
Calibrate orthogonal evidence and uncertainty
04
Select a diverse set under experimental constraints
05
Deliver assay-ready files and a data-return schema
CAT SOLUTIONS
1 defined solution packages.
AI Product Atlas
SUR-COR-001 · Reproduced computational demonstration
Corrosion-inhibitor Adsorption on Fe(110) + FMO
candidates
AI-guided Materials R&D research and development
Corrosion-inhibitor Adsorption on Fe(110) + FMO
This demonstration uses reproducible structure relaxation and electronic-structure descriptors to establish a relative ranking and a testable mechanism. Absolute energies or affinities are not presented as experimental truth.
Computational role
Converge candidate structures, identify stable configurations, and expose the descriptors that drive the ranking.
Decision output
4 configurations → 4 UMA relaxations → N-down → mechanism report
Evidence boundary
Final performance requires project-specific synthesis, electrochemical, surface, binding, or release validation.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Material objective and reference states
Define candidates, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Composition/structure enumeration
Generate a traceable candidate space appropriate to corrosion-inhibitor adsorption screening.
candidate-space file and provenance03 · Relaxation and descriptor calculation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · DFT/experimental calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Constrained candidate selection
Select a diverse, capacity-matched set for AI-guided Materials R&D research and development.
ranked experimental package06 · Synthesis and characterization
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Closed-loop materials update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
MAT-IBF · PRODUCT LINE
InterfaceBind-AI
Interface Binding and Charge Transfer
Designed to answer one actionable R&D question: which candidates, controls, and validation experiments should advance together?
DECISION WORKFLOW
Five evidence-preserving steps.
01
Lock the input, endpoint, controls, and exclusions
02
Generate a traceable candidate space
03
Calibrate orthogonal evidence and uncertainty
04
Select a diverse set under experimental constraints
05
Deliver assay-ready files and a data-return schema
CAT SOLUTIONS
1 defined solution packages.
AI Product Atlas
SUR-INT-001 · Reproduced computational demonstration
Drug–Oxide/Carrier Binding
Drug ×
and
Drug–Oxide/Carrier Binding
This demonstration uses reproducible structure relaxation and electronic-structure descriptors to establish a relative ranking and a testable mechanism. Absolute energies or affinities are not presented as experimental truth.
Computational role
Converge candidate structures, identify stable configurations, and expose the descriptors that drive the ranking.
Decision output
2 → 2 relaxations → ΔGbind → p
Evidence boundary
Final performance requires project-specific synthesis, electrochemical, surface, binding, or release validation.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Material objective and reference states
Define Drug ×, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Composition/structure enumeration
Generate a traceable candidate space appropriate to interface binding and charge transfer.
candidate-space file and provenance03 · Relaxation and descriptor calculation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · DFT/experimental calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Constrained candidate selection
Select a diverse, capacity-matched set for and.
ranked experimental package06 · Synthesis and characterization
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Closed-loop materials update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
MAT-COA · PRODUCT LINE
Coating-AI
Coatings, Passivation, and Antifouling
Designed to answer one actionable R&D question: which candidates, controls, and validation experiments should advance together?
DECISION WORKFLOW
Five evidence-preserving steps.
01
Lock the input, endpoint, controls, and exclusions
02
Generate a traceable candidate space
03
Calibrate orthogonal evidence and uncertainty
04
Select a diverse set under experimental constraints
05
Deliver assay-ready files and a data-return schema
CAT SOLUTIONS
1 defined solution packages.
AI Product Atlas
SUR-COA-001 · Illustrative planning example
Self-assembled Monolayer Orientation on Metal Surfaces
Project-defined target and comparator set
and
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Material objective and reference states
Define Project-defined target and comparator set, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Composition/structure enumeration
Generate a traceable candidate space appropriate to coatings, passivation, and antifouling.
candidate-space file and provenance03 · Relaxation and descriptor calculation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · DFT/experimental calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Constrained candidate selection
Select a diverse, capacity-matched set for and.
ranked experimental package06 · Synthesis and characterization
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Closed-loop materials update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
MAT-FMO · PRODUCT LINE
FMO-AI
Frontier-orbital and Reactivity Fingerprints
Designed to answer one actionable R&D question: which candidates, controls, and validation experiments should advance together?
DECISION WORKFLOW
Five evidence-preserving steps.
01
Lock the input, endpoint, controls, and exclusions
02
Generate a traceable candidate space
03
Calibrate orthogonal evidence and uncertainty
04
Select a diverse set under experimental constraints
05
Deliver assay-ready files and a data-return schema
CAT SOLUTIONS
1 defined solution packages.
AI Product Atlas
MOL-FMO-001 · Illustrative planning example
Molecular-library HOMO/LUMO/χ/η/ω Fingerprint
Project-defined target and comparator set
and Materials
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Material objective and reference states
Define Project-defined target and comparator set, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Composition/structure enumeration
Generate a traceable candidate space appropriate to frontier-orbital and reactivity fingerprints.
candidate-space file and provenance03 · Relaxation and descriptor calculation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · DFT/experimental calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Constrained candidate selection
Select a diverse, capacity-matched set for and Materials.
ranked experimental package06 · Synthesis and characterization
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Closed-loop materials update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
MAT-BND · PRODUCT LINE
Binding-AI
Host–Guest and Cluster Binding
Designed to answer one actionable R&D question: which candidates, controls, and validation experiments should advance together?
DECISION WORKFLOW
Five evidence-preserving steps.
01
Lock the input, endpoint, controls, and exclusions
02
Generate a traceable candidate space
03
Calibrate orthogonal evidence and uncertainty
04
Select a diverse set under experimental constraints
05
Deliver assay-ready files and a data-return schema
CAT SOLUTIONS
1 defined solution packages.
AI Product Atlas
MOL-BND-001 · Illustrative planning example
Host–Guest/Cluster ΔGbind and Ka
× designs
and
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Material objective and reference states
Define × designs, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Composition/structure enumeration
Generate a traceable candidate space appropriate to host–guest and cluster binding.
candidate-space file and provenance03 · Relaxation and descriptor calculation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · DFT/experimental calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Constrained candidate selection
Select a diverse, capacity-matched set for and.
ranked experimental package06 · Synthesis and characterization
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Closed-loop materials update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
MAT-CHG · PRODUCT LINE
ChargeXfer-AI
Difference Density and Charge Transfer
Designed to answer one actionable R&D question: which candidates, controls, and validation experiments should advance together?
DECISION WORKFLOW
Five evidence-preserving steps.
01
Lock the input, endpoint, controls, and exclusions
02
Generate a traceable candidate space
03
Calibrate orthogonal evidence and uncertainty
04
Select a diverse set under experimental constraints
05
Deliver assay-ready files and a data-return schema
CAT SOLUTIONS
1 defined solution packages.
AI Product Atlas
MOL-CHG-001 · Illustrative planning example
Adsorption/Complexation Δρ + Bader Net Charge
Project-defined target and comparator set
structures
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Material objective and reference states
Define Project-defined target and comparator set, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Composition/structure enumeration
Generate a traceable candidate space appropriate to difference density and charge transfer.
candidate-space file and provenance03 · Relaxation and descriptor calculation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · DFT/experimental calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Constrained candidate selection
Select a diverse, capacity-matched set for structures.
ranked experimental package06 · Synthesis and characterization
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Closed-loop materials update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
MAT-MOF · PRODUCT LINE
MOFCapture-AI
Porous-material Adsorption Screening
Designed to answer one actionable R&D question: which candidates, controls, and validation experiments should advance together?
DECISION WORKFLOW
Five evidence-preserving steps.
01
Lock the input, endpoint, controls, and exclusions
02
Generate a traceable candidate space
03
Calibrate orthogonal evidence and uncertainty
04
Select a diverse set under experimental constraints
05
Deliver assay-ready files and a data-return schema
CAT SOLUTIONS
1 defined solution packages.
AI Product Atlas
CRY-MOF-001 · Illustrative planning example
MOF/Zeolite CO₂·H₂ Adsorption Energy
wells Materialsstructures
and
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Material objective and reference states
Define wells Materialsstructures, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Composition/structure enumeration
Generate a traceable candidate space appropriate to porous-material adsorption screening.
candidate-space file and provenance03 · Relaxation and descriptor calculation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · DFT/experimental calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Constrained candidate selection
Select a diverse, capacity-matched set for and.
ranked experimental package06 · Synthesis and characterization
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Closed-loop materials update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
MAT-POL · PRODUCT LINE
Polymorph-AI
Molecular-crystal Polymorph Ranking
Designed to answer one actionable R&D question: which candidates, controls, and validation experiments should advance together?
DECISION WORKFLOW
Five evidence-preserving steps.
01
Lock the input, endpoint, controls, and exclusions
02
Generate a traceable candidate space
03
Calibrate orthogonal evidence and uncertainty
04
Select a diverse set under experimental constraints
05
Deliver assay-ready files and a data-return schema
CAT SOLUTIONS
1 defined solution packages.
AI Product Atlas
CRY-POL-001 · Illustrative planning example
API Polymorph Lattice-energy Ranking
candidatespolymorphs
Drugpolymorphs
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Material objective and reference states
Define candidatespolymorphs, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Composition/structure enumeration
Generate a traceable candidate space appropriate to molecular-crystal polymorph ranking.
candidate-space file and provenance03 · Relaxation and descriptor calculation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · DFT/experimental calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Constrained candidate selection
Select a diverse, capacity-matched set for Drugpolymorphs.
ranked experimental package06 · Synthesis and characterization
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Closed-loop materials update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
AI Product Atlas
MAT-STB · PRODUCT LINE
Stability-AI
Formation Energy and Phase Stability
Designed to answer one actionable R&D question: which candidates, controls, and validation experiments should advance together?
DECISION WORKFLOW
Five evidence-preserving steps.
01
Lock the input, endpoint, controls, and exclusions
02
Generate a traceable candidate space
03
Calibrate orthogonal evidence and uncertainty
04
Select a diverse set under experimental constraints
05
Deliver assay-ready files and a data-return schema
CAT SOLUTIONS
1 defined solution packages.
AI Product Atlas
CRY-STB-001 · Illustrative planning example
New-composition Formation Energy and Distance to Hull
Project-defined target and comparator set
New Materials Discovery
What a project-specific case will contain
The displayed candidate counts illustrate the operating scale and expected decision funnel. They are replaced by versioned customer data, explicit assay controls, and prespecified acceptance gates at project start.
IMPLEMENTATION PLAN
Seven stages from project lock to closed-loop learning.
StageExecutionGate / output
01 · Material objective and reference states
Define Project-defined target and comparator set, success metrics, controls, data versions, and hard exclusions.
versioned project manifest02 · Composition/structure enumeration
Generate a traceable candidate space appropriate to formation energy and phase stability.
candidate-space file and provenance03 · Relaxation and descriptor calculation
Score orthogonal evidence streams separately; preserve uncertainty, disagreement, and applicability limits.
evidence matrix with uncertainty04 · DFT/experimental calibration
Apply predeclared quality gates, leakage checks, robustness tests, and benchmark comparisons.
decision gate and exclusion log05 · Constrained candidate selection
Select a diverse, capacity-matched set for New Materials Discovery.
ranked experimental package06 · Synthesis and characterization
Return raw measurements, QC flags, controls, batches, and failures in the supplied schema.
assay-ready data-return template07 · Closed-loop materials update
Retrain or recalibrate only after assay QC; propose the next experiment on a Pareto basis.
updated recommendation and next-round designPROJECT HANDOFF
Files designed for immediate experimental use.
Ranked candidates, controls, sequence/structure/image identifiers, and selection rationale.
Separate evidence channels, uncertainty, applicability domain, exclusions, and version lineage.
Plate map or sample/ROI matrix, assay metadata, replicates, randomization, and QC rules.
Raw signal, normalized endpoint, batch, failure reason, and analysis-ready tables for the next round.
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