Monte Carlo sampling and multivariate adaptive regression splines as tools for QSAR modelling of HIV-1 reverse transcriptase inhibitors

Abstract

The present work focuses on the development of an interpretable quantitative structure–activity relationship (QSAR) model for predicting the anti-HIV activities of 67 thiazolylthiourea derivatives. This set of molecules has been proposed as potent HIV-1 reverse transcriptase inhibitors (RT-INs). The molecules were encoded to a diverse set of molecular descriptors, spanning different physical and chemical properties. Monte Carlo (MC) sampling and multivariate adaptive regression spline (MARS) techniques were used to select the most important descriptors and to predict the activity of the molecules. The most important descriptor was found to be the aspherisity index. The analysis of variance (ANOVA) and interpretable spline equations showed that the geometrical shape of the molecules has considerable effect on their activities. It seems that the linear molecules are more active than symmetric top compounds. The final MARS model derived displayed a good predictive ability judging from the determination coefficient corresponding to the leave multiple out (LMO) cross-validation technique, i.e. r 2 = 0.828 (M = 12) and r 2 = 0.813 (M = 20). The results of this work showed that the developed spline model is robust, has a good predictive power, and can then be used as a reliable tool for designing novel HIV-1 RT-INs.