Hybrid docking-QSAR methodology in prediction of HIV-1 protease inhibitory activities of some drug candidates

In this study, application of a new hybrid docking-quantitative structure activity relationship (QSAR) methodology to model and predict the HIV-1 protease inhibition activities of a series of newly synthesized chemicals is reported. This approach can provide valuable information about the most important chemical and structural features of the ligands that affect their inhibitory activities. Docking studies were used to find the actual conformations of chemicals in active site of HIV-1 protease. Then the molecular descriptors were calculated from these conformations. Multiple linear regression (MLR) and least square support vector machine (LS-SVM) were used as QSAR models, respectively. The obtained results reveal that statistical parameters of the LS-SVM model are better than the MLR model, which indicate that there are some non-linear relations between selected molecular descriptors and anti-HIV activities of interested chemicals. Keyword: AutoDockvina .BINANA .drug design . HIV-1 protease inhibitors .quantitative structure activity relationship . the docking derived descriptors . 1Introduction: HIV-1 (Human Immunodeficiency Virus Type-1) is the pathogenic retrovirus and causative organism of the acquired immunodeficiency syndrome (AIDS). The last stage in the HIV life cycle is transformation from an immature non-infectious viron to a mature infectious virus which is catalyzed by viral protease (PR) [1]. HIV-1 protease inhibitors (PIs) are promising targets for anti-HIV drug design [2]. Quantitative structure-activity relationship (QSAR) is the favorite methodology to screen the best candidate of anti-HIV drugs. Two main