Design and Discovery of New HIV‐1 RT Inhibitors Using Generative AI, Virtual Screening, and Molecular Dynamics Simulations

Abstract

ABSTRACT The era of generative artificial intelligence (AI) in drug design is here, and in this study, we applied a generative AI model to design potential non‐nucleoside inhibitors of HIV‐1 reverse transcriptase (RT). RT converts viral RNA into DNA, facilitating viral integration into a host's genome. Inhibiting RT is therefore critical in combating HIV‐1. Using the REINVENT prior model, we fine‐tuned it with classified RT inhibitors (pIC50 > 8.5) from ChEMBL, generating over 6000 compounds. These compounds underwent rigorous filtering, including classification by a Message Passing Neural Network (MPNN), PAINS filtering, pharmacophore modeling, structure‐based screening, MMGBSA scoring, and ADMET prediction. We identified five top‐performing compounds, which demonstrated superior docking scores (≤ −13.22 kcal/mol) and binding free energies (MMGBSA dG Bind ≤ −77.48 kcal/mol) compared to the reference ligand (−8.42 kcal/mol). Further ADMET predictions and molecular dynamics simulation at 500 ns revealed that these compounds had better drug‐like properties and comparable stability at the active site compared to the reference ligand. These findings suggest that the identified compounds are promising candidates for further in vitro validation to confirm their therapeutic potential against HIV‐related proteins. This study highlights the transformative role of AI‐driven drug discovery in addressing HIV drug resistance.