Innovative integration of molecular docking and machine learning for drug discovery: from virtual screening to nanomolar inhibitors

Clinical Snapshot

Key Findings

• P Value: Not applicable for review methodology

• Effect Size: Discovery of nanomolar inhibitors against multiple targets

• Primary Outcome: Development of integrated computational workflow for drug discovery

• Nnt Or Sensitivity: Nanomolar potency achieved for STAT3, TTK, LSD-1, and HER2 inhibitors

• Confidence Interval: Not provided

Clinical Application

Requires computational expertise and access to molecular modeling software Relevant to Australian pharmaceutical research institutions and biotech companies. Could support TGA drug development pathways by improving early-stage compound identification and reducing development costs Pharmaceutical researchers and drug discovery teams globally

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.

References

1. 1.Taha, M. O., & Daoud, S. (2026). Innovative integration of molecular docking and machine learning for drug discovery: from virtual screening to nanomolar inhibitors. Chemical Communications, 62(28), 4156-4169. https://doi.org/10.1039/d5cc06025g