The recent outbreak of the coronavirus disease 2019 (COVID-19) caused by SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) in the last few months raised global health concern. Previous research described that remdesivir and ritonavir can be used as effective drugs against COVID-19. In this study, we applied the structure-based virtual screening (SBVS) on the high similar remdesivir- and ritonavir-approved drugs, selected from the DrugBank database as well as on a series of ritonavir derivatives, selected from the literature. The aim was to provide new potent SARS-CoV-2 main protease (Mpro) inhibitors with high stability. The analysis was performed using AutoDock VINA implicated in the PyRx 0.8 tool. Based on the ligand binding energy, 20 compounds were selected and then analyzed by AutoDock tools. Among the 20 compounds, 3 compounds were selected as high-potent anti-COVID-19.

Quantitative Structure Activity Relationship (QSAR) analysis techniques are tools largely utilized in many research fields, including drug discovery processes. In this work electronic descriptors are calculated with the Gaussian 03W software using the DFT method with the BecKe 3-parameters exchange functional and Lee-Yang-Parr correlation functional, with Kohn and Sham orbitals (KS) developed on a Gaussian Basis of type 6-31G (d), in combination with five Lipinski parameters that have been calculated with ChemOffice software, in order to develop a statistically verified 2D-QSAR model able to predict the biological activity of new molecules belonging to the same range of coumarins rather than chemical synthesis and biological evaluations that require more time and resources. Two QSAR models against both MCF-7 and HepG-2 cell lines are obtained using the multiple linear regression method. The predictive power of these models has been confirmed by internal and external validation. The Leverage method was used to determine the domain of applicability of the 2D-QSAR models developed. The results indicate that the best QSAR model is the one that links the 2D descriptors with the CDK inhibitory activity of the cell line (HepG-2) R 2 = 0.748, R 2 cv = 0.618, MSE = 0.03 for the learning series and R 2 = 0.73, MSE = 0.18 for the test series. This model implies that coumarin inhibitory activity is strongly related to dipole moment and the number of hydrogen bond donors. The results obtained suggest the importance of studying structure-activity relationships as a principal axis in drug design. The docking procedure using AutoDOCK Tools was also used to understand the mechanisms of molecular interactions and consequently, to develop new inhibitors.

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.

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