Innovative Integration of Molecular Docking and Machine Learning for Drug Discovery: From Virtual Screening to Nanomolar Inhibitors

Abstract

This review highlights our group’s systematic approach to integrating molecular docking, pharmacophore modeling, and machine learning methodologies for the rational discovery of bioactive leads. We describe innovative strategies including docking-based data augmentation, ligand-receptor contact fingerprints, genetic algorithm-guided feature selection, and SHAP-based model interpretation. These approaches have enabled the discovery of nanomolar inhibitors against multiple therapeutic targets including STAT3, TTK, LSD-1, and HER2. The presented workflow demonstrates how machine learning (ML) can be synergistically combined with traditional computer-aided drug design methods to achieve efficient scaffold hopping and identify novel chemotypes with potent biological activities.