Identification of novel HIV-1 integrase inhibitors using shape-based screening, QSAR, and docking approach.

The objective of this study is to identify novel HIV-1 integrase (IN) inhibitors. Here, shape-based screening and QSAR have been successfully implemented to identify the novel inhibitors for HIV-1 IN, and in silico validation is performed by docking studies. The 2D QSAR model of benzodithiazine derivatives was built using genetic function approximation (GFA) method with good internal (cross-validated r(2)  = 0.852) and external prediction (). Best docking pose of highly active molecule of the benzodithiazine derivatives was used as a template for shape-based screening of ZINC database. Toxicity prediction was also performed using Deductive Estimation of Risk from Existing Knowledge (DEREK) program to filter non-toxic molecules. Inhibitory activities of screened non-toxic molecules were predicted using derived QSAR models. Active, non-toxic screened molecules were also docked into the active site of HIV-1 IN using AutoDock and dock program. Some molecules docked similarly as highly active molecule of the benzodithiazine derivatives. These molecules also followed the same docking interactions in both the programs. Finally, four benzodithiazine derivatives were identified as novel HIV-1 integrase inhibitors based on QSAR predictions and docking interactions. ADME properties of these molecules were also computed using Discovery Studio.