A Robust Ensemble Machine Learning Approach for Inhibitor Discovery: Case Study of HIV‐1 NNRTI and Validation Using MD Simulation

The growing demand for new therapeutics highlights the need for intelligent, cost‐effective, and scalable drug discovery strategies. Here, we present an artificial intelligence (AI)‐based ensemble framework to accelerate the identification of small‐molecule inhibitors against therapeutic targets. As a case study, we applied this approach to HIV‐1 reverse transcriptase (HIV‐1 RT), an essential enzyme in viral replication. Our stacking ensemble model, trained on a curated ChEMBL dataset, achieved high predictive performance (90.3% accuracy, 89.4% ROC‐AUC) and was used to screen the Natural Products Atlas (NPA) database. Promising hits were evaluated through physicochemical and ADMET filters, molecular docking, and 1 µs molecular dynamics (MD) simulations. Compound NP1, which exhibited stable binding to the NNRTI binding pocket, outperformed the FDA‐approved drug doravirine in post‐MD characterizations. Network analysis further suggested potential allosteric regulation via residues N136 and E138. This flexible AI‐MD pipeline provides an efficient strategy for discovering and repurposing inhibitors, with broad applicability to other therapeutic targets.