Identification of potential α-glucosidase inhibitors: 3D-QSAR modeling, molecular docking approach

Abstract

In search of the potent drugs as an α -glucosidase inhibitors, we have studied a series of twenty-five triazinoindole-based thiosemicarbazide analogs using two important computational approaches namely 3D-QSAR and molecular docking. In this study, 3D-QSAR model was established using the CoMFA and the CoMSIA techniques. The best CoMFA and CoMSIA models were developed using 20 compounds in the training set afforded Q 2 values of 0.537and 0.709 respectively, and R 2 values of 0.977 and 0.965 respectively. In order to check the robustness of the CoMFA and the CoMSIA models, we executed an external validation using 5 molecules; the R 2 test values obtained are 0.871 and 0.695 respectively. Likewise, CoMFA and CoMSIA contour maps afforded much helpful information to determine the structural requirements that influence the activity, thus, these results enabled us to propose new scaffolds with important α -glucosidase inhibitors. Molecular docking study was conducted to identify the key residues between triazinoindole-based thiosemicarbazide scaffolds and α -glucosidase receptor (PDB code: 3TOP).