In-silico Discovery and Simulated Selection of Multi-target Anti-HIV-1 Inhibitors

Abstract

The multi-target quantitative structure-activity relationship (mt-QSAR) study of human immunodeficiency virus (HIV-1) inhibitors was addressed by applying a modest, hitherto active linear regression model based on the Genetic function approximation. QSAR studies were performed on two datasets of HIV-1 inhibitors targeted on integrase and reverse transcriptase, respectively. By using the genetic function approximation method, the collaboration among different set of inhibitors was exploited and an efficient multi-target QSAR modeling for HIV-1 inhibitors was obtained. The predictive quality of the mt-QSAR models was tested for an external set of 30 compounds, randomly chosen out of 150 compounds. The linear regression model based on the Genetic function approximation with eight selected descriptors was obtained. The accuracy of the proposed model is illustrated using the following evaluation techniques: cross-validation, validation through an external test set, applicability domain, and Y-randomization. We accordingly propose a quantitative model, and we interpret the activity of the compounds relying on the multivariate statistical analysis. This study shows that the prediction results demonstrated that the predictive capacity of the model was attractive, and it can be utilized for outlining comparable gathering of anti-HIV compounds.