Target Identity & Biology Audit
Resolve isoforms, domains, paralogs, subcellular location, tissue and cell-type expression, disease association, genetic support and intended direction of modulation.
Target Druggability Assessment
Assess whether a disease-relevant target can be modulated by the right therapeutic modality—and identify the evidence gaps that matter before costly discovery work begins.
Assess Your TargetOVERVIEW
Druggability is modality-, mechanism- and context-dependent
A target may have a ligandable pocket yet lack disease relevance, tissue selectivity or a safe therapeutic window. Conversely, a protein without a conventional small-molecule pocket may be tractable through an antibody, degrader, oligonucleotide or other modality. Our assessment connects structural, chemical, cellular, genetic and safety evidence to a clearly defined target product hypothesis.
We distinguish ligandability (capacity to bind a modulator), tractability (availability of a feasible discovery route) and druggability (prospect of achieving a therapeutically useful effect). Scores support portfolio decisions; they do not prove efficacy, causal disease biology or clinical success.
The service can evaluate a single target, compare a short list or add tractability evidence after Target Discovery from Omics Data.
Required inputs and supported scope
- Targets: human proteins preferred; mouse, rat, pathogen and other well-annotated species supported with orthology caveats.
- Identifiers: gene/protein IDs, isoform, sequence, species and disease/indication; intended activation, inhibition, degradation or replacement mechanism.
- Structures: PDB/mmCIF, cryo-EM/X-ray models or predicted structures with confidence metrics; relevant complexes and conformational states.
- Optional project data: FASTA, SDF/SMILES, assay tables, binding/activity values, expression matrices, variant data, proteomics and phenotypic screens.
- Necessary metadata: assay units and conditions, construct, species, tissue/cell context, controls, replicates, batch, platform and provenance.
CORE SERVICES
A multi-axis assessment matched to therapeutic modality
Structure & Pocket Assessment
Review experimental and predicted structures, model confidence, missing regions, assemblies, conformational states and pocket geometry; assess pocket conservation and dynamics where data permit.
Chemical & Ligand Evidence
Map known ligands, potency, selectivity, assay type, structure–activity evidence, covalent opportunities and close-family chemical precedent with careful treatment of inconsistent units and assay contexts.
Modality Tractability
Evaluate small molecule, antibody, targeted degradation and nucleic-acid routes using localization, epitope/accessibility, turnover, complex formation and tissue-delivery requirements.
Selectivity & Safety Risk
Assess paralog homology, conserved pockets, essentiality, baseline tissue expression, loss-of-function tolerance, pathway liabilities and known target-class safety signals.
Evidence Scoring & Gap Analysis
Deliver transparent, modality-specific scores with evidence provenance, contradictions, uncertainty, applicability domain and experiments that can change the decision.
INTEGRATED WORKFLOW
Six stages from target definition to experimental de-risking
| Stage | Key Activities | Decision Output |
|---|---|---|
| 1. Target Product Hypothesis | Define indication, patient/tissue context, target isoform, desired mechanism, therapeutic modality, comparator and acceptance criteria. | Assessment question and modality-specific decision gates. |
| 2. Identity & Evidence Audit | Harmonize identifiers and assay units; audit structures, ligands, expression, genetics, safety and provenance; identify conflicting or missing evidence. | Evidence inventory, confidence map and data gaps. |
| 3. Structural & Chemical Analysis | Characterize pockets, interfaces, ensembles, known binders, physicochemical context, family precedent and selectivity constraints. | Ligandability hypotheses and chemical starting-point assessment. |
| 4. Modality & Exposure Analysis | Match accessibility, localization, turnover, complex biology and delivery constraints to small molecule, antibody, degrader or nucleic-acid routes. | Feasible modalities, route-specific risks and alternatives. |
| 5. Integrated Scoring | Combine biology, tractability, selectivity and safety evidence without double-counting correlated sources; perform sensitivity and uncertainty analysis. | Ranked evidence profile, contradictions and go/no-go criteria. |
| 6. Validation Planning | Design orthogonal binding, engagement, functional, selectivity and disease-relevant experiments with appropriate controls and replicates. | Prioritized validation plan and decision-changing experiments. |
Statistical and AI controls: Custom experimental datasets are reviewed for biological replication, controls, batch–condition confounding, effect size, uncertainty and multiplicity. For predictive models, target homologs and shared protein families are grouped where appropriate to avoid train–test leakage; preprocessing and feature selection remain within training folds. Nested or repeated cross-validation, class imbalance, positive–unlabelled assumptions, calibration and external benchmarks are reported. Predictions outside represented target families, modalities, species or assay domains are flagged rather than extrapolated silently.
DELIVERABLES
Traceable outputs for target selection and modality choice
Target Identity & Context Dossier
Canonical identifiers, isoforms, domains, localization, tissue/cell expression, homologs and disease-context summary.
Structure & Pocket Portfolio
Structure provenance, confidence and state coverage; annotated pockets/interfaces, geometry, conservation and caveats.
Chemical Evidence Table
Known ligands, activity data, assay context, structures, selectivity evidence, family precedent and data-quality flags.
Modality Tractability Matrix
Small molecule, antibody, degrader and nucleic-acid feasibility with route-specific opportunities and constraints.
Integrated Risk & Scorecard
Transparent score components, safety/selectivity risks, contradictory evidence, uncertainty and applicability limits.
Validation & De-risking Plan
Ranked experiments, controls, orthogonal assays, decision thresholds, software/database versions and reproducible methods.
APPLICATIONS
Druggability questions across discovery portfolios
Single-Target Triage
Determine whether the evidence supports a feasible route and which experiment should precede screening investment.
Target Portfolio Comparison
Compare candidates using consistent, modality-specific criteria without converting heterogeneous evidence into a false universal score.
Undrugged & Difficult Targets
Explore interfaces, allosteric sites, covalent residues, extracellular epitopes or degradation strategies while retaining uncertainty.
Modality Selection
Choose between small molecules, antibodies, degraders and nucleic-acid approaches using target location, mechanism and delivery context.
SCIENTIFIC EVIDENCE
Open methods inform—and constrain—the assessment
Open Targets integrates target–disease evidence and tractability annotations across genetics, expression, known drugs and other sources, supporting systematic—but evidence-dependent—target prioritization.1
DrugnomeAI illustrates a genome-wide positive–unlabelled framework that combines gene-level evidence and modality-specific labels. Its design highlights why historical labels, class imbalance, correlated features and evaluation leakage must be considered before applying a score to a new target family.2
Structure-based studies show that pocket druggability depends on 3D geometry and physicochemical context and can change across conformations; one static structure is therefore insufficient when flexibility or assembly state is material.3
1 Buniello, A.; et al. Open Targets Platform: facilitating therapeutic hypotheses building in drug discovery. Nucleic Acids Research 2025, 53, D1467–D1475. https://doi.org/10.1093/nar/gkae1128. Open Access under CC BY 4.0.
2 Raies, A.; et al. DrugnomeAI is an ensemble machine-learning framework for predicting druggability of candidate drug targets. Communications Biology 2022, 5, 1291. https://doi.org/10.1038/s42003-022-04245-4. Open Access under CC BY 4.0.
3 Loving, K. A.; et al. Structure-based druggability assessment of the mammalian structural proteome with inclusion of light protein flexibility. PLoS Computational Biology 2014, 10, e1003741. https://doi.org/10.1371/journal.pcbi.1003741. Open Access under CC BY 4.0.
PROJECT STRATEGY
Turn uncertainty into a focused de-risking plan
Modality before score
We avoid labeling a target simply druggable or undruggable; each conclusion is tied to a mechanism, modality, disease context and evidence threshold.
Evidence before prediction
Experimental structures, binding and functional data are separated from homology, database precedent and model-derived hypotheses.
Decision-changing validation
Recommendations prioritize experiments that can resolve the largest uncertainty: binding-site confirmation, target engagement, selectivity, perturbation phenotype or safety window.
A favorable computational profile is not evidence of therapeutic efficacy or acceptable safety. Pocket scores do not prove compoundability; genetic association does not by itself establish direction of intervention; and absence of known ligands may reflect limited study rather than true intractability. To discuss your target and intended modality, please Contact Us or use the Online Inquiry below.
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