Predicting anti-HIV activity of 1,3,4-thiazolidinone derivatives: 3D-QSAR approach

HIV-1 (human immunodeficiency virus type-1) is the pathogenic retrovirus and causative agent of AIDS. HIV-1 RT is one of the key enzymes in the duplication of HIV-1. Inhibitors of HIV-1 RT are classified as NNRTIs and NRTIs. NNRTIs bind to a region that is not associated with the active site of the enzyme. Within the NNRTIs category, there is a set of inhibitors commonly referred to as thiazolidinone derivatives. The present 3D-QSAR study attempts to explore the structural requirements of thiazolidinone derivatives for anti-HIV activity. Based on the structures and biodata of previous thiazolidinone analogs, 3D-QSAR studies have been performed with a training set consisting of 96 molecules, which resulted in two reliable computational models, CoMFA and CoMSIA with r(2) values of 0.931 and 0.972, standard error of estimation (SEE) of 0.173 and 0.089, and q(2) values of 0.663 and 0.784, respectively, with the number of partial least-squares (PLS) components being six. It is shown that the steric and electrostatic properties predicted by CoMFA contours and the hydrogen bond acceptor, hydrogen bond donor, and hydrophobic properties predicted by CoMSIA contours are related to anti-HIV activity. The predictive ability of the resultant model was evaluated using a test set comprising of 17 molecules and the predicted r(2) values of CoMFA and CoMSIA models were 0.861 and 0.958, respectively. These models are more significant guide to trace the features that really matter especially with respect to the design of novel compounds.