Regression QSAR Models for Predicting HIV-1 Integrase Inhibitors

Abstract

The Human Immunodeficiency Virus (HIV) infection is a global pandemic that has claimed 33 million lives to date. One of the most efficacious treatment for naïve or pre-treated HIV patients is with the HIV integrase strand transfer inhibitors (INSTIs). However, given that HIV treatment is lifelong, the emergence of HIV-1 strains resistant to INSTIs is an imminent challenge. In this work, we showed two best regression QSAR models that were constructed using a boosted Random Forest algorithm (r2 = 0.998, q210CV = 0.721, q2external_test = 0.754) and a boosted K* algorithm (r2 = 0.987, q210CV = 0.721, q2external_test = 0.758) to predict the pIC50 values of INSTIs. Subsequently, the regression QSAR models were deployed against the Drugbank database for drug repositioning. The top ranked compounds were further evaluated for their target engagement activity using molecular docking studies and their potential as INSTIs evaluated from our literature search. Our study offers the first example of a large-scale regression QSAR modelling effort for discovering highly active INSTIs to combat HIV infection.