Target Identification & Validation
Most drug programs fail because they pursue the wrong target. CompoundAI's target identification layer integrates multi-omics data - genomics, transcriptomics, proteomics, and metabolomics - with literature-mined disease associations to surface targets that are both mechanistically sound and clinically relevant.
Each candidate target receives a disease relevance score, druggability assessment, and competitive intelligence summary. Programs that look strong in silico but thin in clinical evidence get flagged before resources are committed.
- Multi-omics data integration pipeline
- Disease relevance scoring with confidence intervals
- Druggability assessment against known protein families
Molecular Design & Optimization
With a validated target in hand, CompoundAI's molecular design layer runs structure-based docking, ligand similarity screening, and generative chemistry workflows in parallel - producing an optimized lead series in weeks rather than months.
Binding affinity predictions are calibrated against wet lab assay data from our internal programs, improving accuracy with each validated data point. The feedback loop tightens continuously.
- Structure-based and ligand-based design
- Generative chemistry for novel scaffold exploration
- Selectivity profiling against off-target panel
ADMET Prediction & Safety Flagging
Most development candidates fail in the clinic due to ADMET liabilities - not efficacy. CompoundAI predicts absorption, distribution, metabolism, excretion, and toxicity profiles for every compound before synthesis is prioritized, eliminating late-stage surprises.
Our ADMET models are trained on proprietary in vitro data from our compound library, updated quarterly with new assay results.
- hERG liability, CYP interaction, solubility prediction
- In silico BBB permeability and P-gp efflux
- Hepatotoxicity and genotoxicity flags
Clinical Translation & Trial Design
Taking a molecule into the clinic without a patient stratification strategy is expensive guesswork. CompoundAI's clinical translation layer builds biomarker hypotheses and patient subgroup models from preclinical data, giving clinical teams a testable hypothesis before enrollment begins.
Dose range prediction models, based on PK/PD simulation, reduce dose escalation iterations and get to the efficacious dose faster.
- PK/PD modeling and dose prediction
- Patient stratification biomarker hypotheses
- Clinical endpoint recommendation based on mechanism
