Diverse models for anti-HIV activity of purine nucleoside analogs

Abstract

Background: Purine nucleoside analogs (PNAs) constitute an important group of cytotoxic drugs for the treatment of neoplastic and autoimmune diseases. In the present study, classification models have been developed for the prediction of the anti-HIV activity of purine nucleoside analogs. Results: The topochemical version of superaugmented pendentic index-4 has been proposed and successfully utilized for the development of models. A total of 60 2D and 3D molecular descriptors (MDs) of diverse nature were selected for building the classification models using decision tree (DT), random forest (RF), support vector machine (SVM), and moving average analysis (MAA). The values of most of these descriptors for each of the analogs in the dataset were computed using the Dragon software (version 5.3). An in-house computer program was also employed to calculate additional MDs which were not included in the Dragon software. DT, RF, and SVM correctly classified the analogs into actives and inactives with an accuracy of 89%, 83%, and 78%, respectively. MAA-based models predicted the anti-HIV activity of purine nucleoside analogs with a non-error rate up to 98%. Therapeutic active spans of the suggested MAA-based models not only showed more potency but also exhibited enhanced safety as revealed by comparatively high values of selectivity index (SI). The statistical importance of the developed models was appraised via intercorrelation analysis, specificity, sensitivity, non-error rate, and Matthews correlation coefficient. Conclusions: High predictability of the proposed models clearly indicates an immense potential for developing lead molecules for potent but safe anti-HIV purine nucleoside analogs.