A promising molecule can still stall because of poor solubility, aggregation, instability, immunogenicity risk, or an impractical formulation profile. We compare the liabilities that matter for your modality so your team can choose which candidates to advance, redesign, or test next.
Predict solubility, pI, charge, and hydrophobicity—the properties that shape formulation and dosing.
Model thermal and conformational stability plus aggregation-prone hotspots and Tagg.
Predict T-cell epitopes and deamidation or oxidation liabilities for biologics.
Flag absorption, distribution, metabolic, exposure, and toxicity liabilities for relevant modalities.
Assess expression, viscosity, and downstream process fit; tier candidates for development.
These four related service pages are listed in the provided website outline and can support a focused question or a broader developability program.
You have more leads than resources to develop them. We score the full set and tier candidates so your formulation and scale-up effort lands on the most developable molecules.
Estimate stability, solubility, and aggregation risk before committing to formulation studies, and identify the measurements most likely to distinguish candidates.
Each modality fails differently. We apply the panel that matches your molecule—immunogenicity and viscosity for antibodies, solubility and metabolism for small molecules.
A liability is more useful when the team can still change the sequence, chemistry, format, or candidate choice. We show which risks are supported by several signals and which require a targeted experiment before a decision.
A single-property question, candidate triage, and engineering program need different evidence. We recommend the smallest panel that can answer the decision reliably.
Send the data you already trust. Missing endpoints can be treated as explicit gaps rather than filled with assumptions.
We preserve endpoint-level results, distinguish measured from predicted data, and avoid letting one composite score hide a severe liability.
The recommended candidate path, confidence, major evidence, and unresolved risks in plain language.
Physicochemical, stability, aggregation, immunogenicity, ADMET, or manufacturability results as scoped.
Candidate-by-endpoint comparison with severity, evidence strength, and stage-of-impact notes.
Candidates grouped into advance, monitor, redesign, or deprioritize tiers with rationale.
Annotated structures, sequence liabilities, property charts, and candidate comparison views.
Sequence, chemistry, format, formulation, and confirmatory-test options for addressable risks.
The cycle moves from modality-specific criteria to a risk-ranked shortlist, with checkpoints that keep predictions, experimental data, and decision thresholds aligned.
Request an AssessmentAgree the developability profile and thresholds that matter for your molecule class.
Collect candidate sequences, structures, and any existing stability or formulation data.
Execute physicochemical, stability, aggregation, immunogenicity, and PK/ADMET panels.
Consolidate panels into a risk matrix and rank candidates across all criteria.
Provide the report and mitigation plan, then re-assess engineered variants in the next round.
The authors showed that a panel of early-stage developability assessments—covering aggregation, stability, and expression—can distinguish antibodies that progress from those that fail downstream, supporting developability as a selection criterion rather than an afterthought.
The authors demonstrated a rational, computation-guided approach to improving protein solubility and reducing aggregation propensity by identifying and mutating aggregation-prone regions, validating the value of sequence-based developability prediction.
The right panel depends on the modality, development stage, existing data, and decision the team needs to make.
The panel can cover solubility, charge, hydrophobicity, conformational stability, aggregation propensity, chemical liabilities, immunogenicity risk, ADMET, and manufacturability. The selected endpoints depend on the modality and project decision.
We assess antibodies and other protein therapeutics, peptides, and small molecules. Each modality uses a different set of relevant endpoints and evidence; results are not forced into one universal score.
For biologics, we identify aggregation-prone regions, surface hydrophobic or charge patches, sequence liabilities, and potential T-cell epitopes. These predictions prioritize experimental characterization and do not by themselves establish clinical immunogenicity.
ADMET focuses on absorption, distribution, metabolism, excretion, and toxicity, especially for small molecules. Developability assessment adds modality-specific properties such as solubility, stability, aggregation, viscosity, immunogenicity, and manufacturability.
Useful inputs include candidate sequences or structures, chemical structures where applicable, modality and format, formulation or route goals, known assay data, target concentration, and the decision the team needs to make.
No. Computational assessment prioritizes candidates and identifies likely liabilities, while experimental assays confirm stability, aggregation, solubility, immunogenicity, pharmacokinetics, and manufacturability under relevant conditions.
Each candidate receives an endpoint-level profile, evidence and uncertainty notes, and a risk matrix. Candidate tiers are linked to the program decision, with mitigation options for liabilities that may be addressable.
Share your candidate sequences or structures, modality, known assay data, and the decision your team is facing. CD ComputaBio will propose a focused panel that separates essential endpoints from optional deeper analysis.
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