classical-ml-drug-discovery
About this skill
Build defensible molecular ML for drug discovery — QSAR, virtual screening, ADMET, toxicity, binding affinity, drug-target models — with Random Forest, SVM/SVR, and Gradient Boosting, plus dataset analysis, inductive conformal prediction, multi-model consensus, diversity-aware batch selection, and cheminformatics-aware interpretation. Use for curating bioactivity data, comparing RF/SVM/XGBoost, leakage-resistant validation, library screening, calibrated uncertainty, disagreeing-model reconciliation, diverse assay-batch selection, feature-to-molecule mapping, applicability-domain assessment, or auditing a molecular ML paper.
Security
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