Discovery of new anti-HIV candidate molecules with an AI-based multi-stage system approach using molecular docking and ADME predictions

Abstract

The discovery of novel therapeutic molecules against the Human Immunodeficiency Virus (HIV) remains a critical research priority due to the persistent global impact of the disease. Traditional drug discovery processes are often time-consuming, costly, and limited in predictive capacity at early stages. In this study, we propose a three-stage AI-supported framework that integrates deep learning and molecular docking to accelerate candidate identification. First, a customized Autoencoder-Long Short-Term Memory (LSTM) model was employed to generate novel molecular structures consistent with key pharmacokinetic rules. Second, a Geometric Deep Learning (GDL) model was designed to evaluate interactions with major HIV-1 targets, including integrase, protease, and reverse transcriptase. Finally, In silico docking simulations assessed binding affinities and inhibition constants. The framework generated molecules that not only complied with pharmacokinetic and drug-likeness criteria (e.g., QED, ADME, SAScore) but also demonstrated favorable binding properties, particularly towards HIV-1 reverse transcriptase. These findings highlight the potential of the proposed approach to complement early-stage drug discovery and to contribute to the design of promising lead compounds for further experimental validation.