No abstract

No abstract

No abstract

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.

In this study, quantitative mathematical models were established to understand the relationship between a series of 26 quinazoline / quinoline derivative and their biological activity against DENV virus using 3D-QSAR and HQSAR analysis. According to the results we obtained, the models have good predictability:the HQSAR model (Q 2 = 0.82, R 2 = 0.95), the CoMFA model with Q 2 = 0.755, R 2 = 0.94 and CoMSIA with Q 2 = 0.76, R 2 = 0.930. It should be noted that all three models successfully meet all external validation criteria. Molecular docking and dynamic simulation have been employed to explain the mode of binding between the ligand and the active site of the protein, and assess, justify ligand stability in the active receptor site respectively. The results of this research provide an information base for discovering new inhibitors of DENV virus.

No abstract

Backgroud: Kinases are proteins that control many biological functions. They are involved in cellular regulation, and many of them are deregulated in cancer proliferation. The evidence of this deregulation in many pathologies served as the origin of kinases as a therapeutic class and constitutes the motive that leads numerous teams to search for inhibitors of these targets. Objective: Based on 3D-QSAR studies and the molecular docking approach, we have developed new potential inhibitors that could be optimized and transformed into colon cancer drugs. Objective: Based on 3D-QSAR studies and the molecular docking approach, we have developed new potential inhibitors that could be optimized and transformed into colon cancer drugs. Method: To design new bioactive molecules and study their interactions with the cyclin-depend kinase type 2 (CDK2) enzyme, we used two virtual screening methods: 3D-QSAR modeling and molecular docking on a series of 28 pyrimidine-based benzothiazole derivatives. Results: To develop models (3D QSAR) we used CoMFA and CoMSIA techniques using SYBYL-X2.0 molecular modeling software. The statistical parameters reveal that the good CoMFA model displays (Q²= 0.587; R²= 0.895) and that of CoMSIA displays (Q²= 0.552; R²= 0.768) which are considered to be very good internal prediction values, while an external validation of a test series of 5 compounds not included in the model development series gives R²test values of 0.56 for CoMFA and R²test values of 0.51 for CoMSIA. The molecular docking approach with AutoDockTools-1.5.6 is introduced in this work to enrich the interpretations extracted from the CoMFA and CoMSIA contour maps, and to provide an in silico research method for the most favorable mode of interaction of an inhibitor within its receptor (CDK2). Conclusion: We have constructed and validated a quantitative 3D model of structure-activity relation-ships of pyrimidine-based benzothiazole derivatives as CDK2 inhibitors. This model allows us to identify the nature and position of the groups that enhance the activity, giving us directions to discover new, more powerful molecules in a limited time.