Hybrid docking-QSAR studies of DPP-IV inhibition activities of a series of aminomethyl-piperidones. (October 2016) - Amini, Zohreh; Fatemi, Mohammad Hossein; Gharaghani, Sajjad
Graphical abstract: Highlights: All molecular descriptors were driven from the best docking-derived conformation of molecules. MLR and LM-ANN were applied for modeling of the studied molecules. Developed LM-ANN model was reasonably predicted the DPP-IV inhibitory activities of aminomethyl-piperidones. Abstract: In this study, the dipeptidyl peptidase-IV (DPP-IV) inhibition activities of a series of novel aminomethyl-piperidones were investigated by molecular docking studies and modeled by quantitative structure–activity relationship (QSAR) methodology. Molecular docking studies were used to find the best conformations of the studied molecules in the active site of DPP-IV protein. Then the best docking-derived conformation for each molecule was applied for calculating the molecular descriptors. Multiple linear regression (MLR) and Levenberg–Marquardt artificial neural network (LM-ANN) were conducted on descriptors derived by docking. The results of these models revealed the superiority of LM-ANN model over MLR which showed the nonlinear relationship between the selected molecular descriptors and DPP-IV inhibition activities of studied molecules. The correlation coefficient (R) and standard error (SE) of ANN model were 0.983 and 0.103 for the training set and 0.966 and 0.168 for the external test set. These results showed a close agreement between the experimental and calculated values of p IC50 which demonstrated the robustness of LM-ANN model in modeling of aminomethyl-piperidones. Applicability domain analysis and sensitivity analysis were applied on the obtained models. This study gives useful information for further experimental studies on DPP-IV inhibitors. The results of this work reveal the applicability of hybrid docking-QSAR methodology in ligand-protein studies.