Machine-learning-based virtual screening and ligand docking identify potent HIV-1 protease inhibitors

Abstract

The human immunodeficiency virus type 1 (HIV-1) is a retrovirus that can cause acquired immunodeficiency syndrome (AIDS), severely weakening the immune system. The United Nations estimates that there are 37.7 million people with HIV worldwide. HIV-1 protease (PR) cleaves polyproteins to create the individual proteins that comprise an HIV virion. Inhibiting PR prevents the creation of new virions, rendering PR an attractive antiviral target. In the present study, a machine-learning regression model was constructed to predict pIC50 bioactivity concentrations using data from 2547 experimentally characterized PR inhibitors. The model achieved Pearson correlation coefficient of 0.88, R-squared of 0.78, and a RMSE of 0.717 in pIC50 units on unseen data using 199 high-variance PubChem substructure fingerprints. The SWEETLEAD database of approximately 4300 traditional medicine compounds and drugs from around the world was screened using the model. Fifty molecules were identified as highly potent, with pIC50 of at least 7.301 (IC50 <= 50 nM). Nine of these molecules, such as lopinavir and ritonavir, are known antiviral drugs. The highly potent molecules were ligand-docked to the 3D structure of HIV protease at the active site. Dihydroergotamine mesylate (daechu alkaloids) had a very strong binding affinity of −13.2, outperforming all known antiviral drugs that were tested. It was also predicted by the model to have an IC50 of 9.16 nM, which is considered very low and desirable. Overall, this study demonstrates the use of machine-learning regression models for virtual screening and highlights several drugs with significant promise for repurposing against HIV-1. Future steps include testing dihydroergotamine mesylate and other candidates in vitro.

Keywords

• HIV
• Virtual screening
• Drug repurposing
• Machine learning
• Drug discovery