Molecular Modeling Studies of C-Glycosylfavone Derivatives as GSK-3β Inhibitors Based on QSAR and Docking Analysis

Abstract

Glycogen synthase kinase-3 beta (GSK-3β) is implicated in abnormal hyperphosphorylation of the tau protein and its inhibitors may be a promising therapeutic approach for treating Alzheimer’s disease. Here, a series of C-glycosylfavone derivatives as GSK-3β inhibitors was selected to perform two-dimensional quantitative structure activity relationship (2D-QSAR) method and docking analysis. The 2D-QSAR model was generated and validated using a dataset of 23 compounds and a test set of 5 compounds, respectively. The best model selected by the partial-least-squares (PLS) regression method revealed a regression coefficient (r2) value of 0.85 and the mean-square-error (MSE) value of 0.04. The predictive ability and stability of the generated model was verified by external and internal validations, and gave the regression coefficient values of 0.93 and 0.72, respectively. Molecular docking analysis using AutoDock vina was carried out to explain the binding modes of C-glycosylfavone ligands with the GSK-3β receptor. Based on the obtained results, a novel series of C-glycosylfavone derivative was designed and their activity and binding affinity were predicted. The generated work could be helpful for the design and development of novel GSK-3β inhibitors.