Predictive QSAR modeling on tetrahydropyrimidine-2-one derivatives as HIV-1 protease enzyme inhibitors

QSAR model development of 51 tetrahydropyrimidine-2-one was carried out to predict HIV-1 protease receptors inhibitors activity. Physicochemical parameters were calculated using DRAGON descriptor software, version 5.5. Stepwise multiple linear regression analysis was applied to derive QSAR models, which were further evaluated for statistical significance and predictive power by internal and external validation. The best quantitative structure activity relationship model having a correlation coefficient (R 2) of 0.824, cross-validated correlation coefficient (Q 2) of 0.773, and \( R_{pred}^{2} \) of 0.910 was selected. The predictive ability of the selected model was also confirmed by leave-one-out cross-validation. The QSAR model indicates that the descriptors (RDF010u, RDF010m, TPSA (NO), F04[C–N]) play an important role in enzyme binding. The information derived from the present study may be useful in the design of more potent substituted tetrahydropyrimidine-2-one.