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For their biological properties and particularly for their anticancer activities, chalcones are widely studied. In this work, we have submitted diverse sets of chalcone derivatives to the 3D-QSAR (3-dimensional quantitative structural-activity relationship) to study their anticancer activities against HTC116 (human colon cancer), relying on the 3-dimensional descriptors: steric and electrostatic descriptors for the CoMFA (comparative molecular field analysis) method and steric, electrostatic, hydrophobic, H-bond donor, and H-bond acceptor descriptors for the CoMSIA method. CoMFA as well as the CoMSIA model have encouraging values of the cross-validation coefficient (Q2) of 0.608 and 0.806 and conventional correlation coefficient (R2) of 0.960 and 0.934, respectively. Furthermore, values of R2test have been obtained as 0.75 and 0.90, respectively. Besides, y-randomization test was also performed to validate our 3D-QSAR models. Based on these satisfactory results, ten new compounds have been designed and predicted by in silico ADMET method. This study could expand the understanding of chalcone derivatives as anticancer agents and would be of great help in lead optimization for early drug discovery of highly potent anticancer activity.

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 (r 2 ) 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.