Building a Scaffold-Split Random Forest QSAR Co-Scientist for EGFR Inhibitor Discovery Using ChEMBL, RDKit, SHAP, and BRICS

A tutorial demonstrates building an autonomous AI co-scientist for EGFR C797S inhibitor discovery using ChEMBL, RDKit, SHAP, and BRICS. The system mines IC50 data, trains a scaffold-split Random Forest QSAR model, interprets potency drivers with SHAP, and generates novel candidates via BRICS fragment recombination.

In this tutorial, we build an autonomous AI co-scientist for EGFR C797S inhibitor discovery. We resolve the target through ChEMBL and UniProt, then mine IC50 records into a clean pIC50 dataset. We use RDKit to standardize molecules, compute Morgan fingerprints, and train a scaffold-split Random Forest QSAR model. We interpret potency drivers with SHAP, then recombine BRICS fragments to generate and rank novel candidates. The post Building a Scaffold-Split Random Forest QSAR Co-Scientist for EGFR Inhibitor Discovery Using ChEMBL, RDKit, SHAP, and BRICS https://www.marktechpost.com/2026/07/06/building-a-scaffold-split-random-forest-qsar-co-scientist-for-egfr-inhibitor-discovery-using-chembl-rdkit-shap-and-brics/ appeared first on MarkTechPost https://www.marktechpost.com .