Know which patients will respond - before clinical trials
Trials are not average patients. CYP metabolism profiles, pharmacogenomic population breakdowns, and resistance variant flags - in seconds. Avoid advancing candidates that fail in specific populations.
“Which patient populations will respond to this EGFR inhibitor given known CYP2D6 polymorphisms and resistance variants?”
How it works
Submit a candidate and target gene
Provide the SMILES, target gene symbol, and optional ADMET results. The engine cross-references 56 pharmacogene profiles, 135K ClinVar pathogenic variants, and CYP metabolism from upstream ADMET predictions.
Population-level analysis
CYP2D6, CYP2C9, CYP3A4 metabolizer phenotypes by population. Resistance variants affecting the binding site flagged. HGNC gene symbol validation against 44K symbols + 58K aliases.
Clinical viability summary
Which populations will respond, which will metabolize too fast or too slow, where resistance is prevalent. A clear stratification output for clinical planning - not a raw data dump.
Proof
56 pharmacogene documents. 134,940 ClinVar pathogenic variants. 13,252 with affects_binding_site = true.
HGNC gene symbol validation: 44K symbols + 58K aliases. CYP substrate analysis from ADMET results (CYP3A4, CYP2D6, CYP2C9 substrate probabilities).
Pre-computed omics data. Results in seconds for any target gene.
Use this when you need to
Identify responder populations before committing to trials
Detect CYP metabolizer risks that could sink a Phase I study
Flag resistance variants that affect binding-site viability
Evaluate clinical viability across populations - not just averages
Reduce clinical trial risk with population-level insight
56 pharmacogenes. 135K resistance variants. Stratify before you commit.