De Novo Design of HIV-1 Integrase-LEDGF/p75 Inhibitors Through Deep Reinforcement Learning and Virtual Screening

Abstract

Human immunodeficiency virus (HIV) has far-reaching impacts on global public health. Acquired immunodeficiency syndrome (AIDS) has caused millions of deaths globally, with thousands still getting infected. Therefore, developing HIV-1 integrase inhibitors is crucial for controlling AIDS by slowing virus replication and transmission. This study is grounded in the framework of deep reinforcement learning, aiming to de novo design inhibitors of HIV-1 integrase-Lens Epithelial-Derived Growth Factor/p75 interaction and subsequently employing molecular docking to screen potential therapeutic compounds. Initially, a molecular generation model was established based on the long short-term memory algorithm and refined through transfer learning to obtain a preliminary generative model. Subsequently, the deep reinforcement learning strategy was employed, using inhibition activity as a reward value, enabling the model more likely to generate molecules with desirable properties. The results indicate that the reinforced generation model not only generates novel and effective SMILES structures with medicinal potential but also demonstrates strong binding affinity between the generated molecules and the target protein, as indicated by molecular docking experiments. Ultimately, through virtual screening, we identified six lead compounds having the potential to become inhibitors of interaction between Lens Epithelial-Derived Growth Factor/p75 and HIV-1 integrase, providing an effective and practical strategy for de novo drug design of HIV-1 integrase inhibitors.