جستجوی مقالات مرتبط با کلیدواژه "comfa" در نشریات گروه "شیمی"

In recent years, drug design for specific diseases has been of great importance for researchers. In this study, 3D-QSAR modeling was used on a series of 1,5-naphthyridine derivatives in order to design new DYRK1A inhibitors as anti-diabetes. After dividing the data set to the training and test sets, the training set was used to generate statistically significant CoMFA (r2cv = 0.376, r2ncv = 0.980) and CoMSIA (r2cv = 0.365, r2ncv = 0.783) models based on the common substructure-based alignment. Furthermore, a set of 9 compounds was created for testing the ability of the CoMFA and CoMSIA models to accurately predict compound activity. Also, the application of the CoMFA focus model provided better results (r2cv = 0.566, r2ncv = 0.988). The design of new analogues based on naphthyridines as DYRK1A inhibitors was carried out using the knowledge obtained from the contours of the CoMFA focus model. Contours were used to identify structural features of this series of analogs that are related to biological activity. Six new designed compounds, in this group of substances, showed stronger DYRK1A inhibitory activity.

To tackle medication resistance in rheumatoid arthritis, type 1 diabetes, and Grave's disease, 32 compounds were chosen as new inhibitors of autoimmune disorders and underwent 2D-QSAR, 3D-QSAR, docking, ADMET, and molecular dynamics (MD) simulation experiments. Genetic approximation-multiple linear regression (GA-MLR) was used in the 2D-QSAR investigation. The experimental activities and those obtained by model 1 were shown to have a respectable connection (r2 = 0.7616 and q2 = 0.6327). The structure-activity relationships (SAR) were statistically studied using the 3D-QSAR technique, which produced strong statistical significance for one high predictive model, comparative molecular field analysis (CoMFA: Q2=0.785; R2=0.936; rext2= 0.818). The steric and electrostatic fields control the bioactivity, according to a thorough examination of the contour maps of the prediction models. This information is very useful in understanding the qualities that must be presented to create new and powerful inhibitors of autoimmune disorders. Through these discoveries, 70 new inhibitors with improved receptor-targeting activity were designed. The last lead compounds were compound 32 and designed compound D40, which were found by virtual screening and subsequent molecular docking. Compounds 32 and D40 have the ability to target proteins such as arginine deiminase 4 (PAD4), major histocompatibility complex (MHC) class II HLA-DQ-ALPHA chain, and thyrotropin receptor (or TSH receptor) proteins, according to the results of the MD simulation for each protein-ligand complex. Our studies suggest that compound 32 and designed compound D40 be studied in vitro and in vivo against some of the selected autoimmune disorders. The MM/GBSA binding free energies are also measured for the selected drugs. For pattern recognition, structural similarity, and hotspots binding energy prediction.

CoMFA and CoMSIA methods were used to perform 3D quantitative structure-activity relationship (3D-QSAR) evaluation and molecular docking, of 5-HT6 receptor inhibitors. The CoMFA model performed on training set in biases of alignment with suitable statistical parameters (q2= 0.556, r2 = 0.836, F= 26.334, SEE=0.171). The best prediction for 5-HT6 receptor inhibitors was obtained by CoMFA (after focusing region) model with highest predictive ability (q2= 0.599, r2 = 0.857, F= 30.853, SEE=0.160) in biases of the same alignment. Using the same alignment, a consistent CoMSIA model was obtained (q2= 0.580, r2 = 0.752, F= 34.361, SEE=0.201) from the three combinations. To evaluate the prediction capability of the CoMFA and CoMSIA models, a test set of 9 compounds was used so that they could show the good predictive r2 values for CoMFA, CoMFA (after focusing region), and CoMSIA models, 0.554, 0.473, and 0.670, respectively. The obtained contour maps form models were used to identify the structural features responsible for the biological activity to design potent 5-HT6 receptor inhibitors. Molecular docking analysis along with the CoMSIA model could reveal the significant role of hydrophobic characteristics in increasing the inhibitors potency. Using the results, some new compounds were designed which showed the higher inhibitory activities as 5-HT6 receptor inhibitors.

3D-QSAR has indeed established itself as a very useful component in the design of compounds with biological potential. The use of this tool will therefore make it possible to more easily target the modulations to be carried out in order to improve the inhibitory capacity of the series studied.Statistical analyses of CoMFA and CoMSIA molecular interaction field descriptors and the model validation methods they generate are presented and applied to the three-dimensional quantitative structure-activity relationships study of a series of 32 wild-type HCT 116 p53 inhibitor styrylquinolines. The selected CoMFA and CoMSIA models were generated by the partial least squares "PLS" method and all had very good internal prediction and cross-validation coefficient values Q² of 0.601 and 0.6 respectively. In view of the results obtained by the contour maps of the developed models as well as the results of molecular docking, new analogues of styrylquinoline were designed.The study of the physicochemical, pharmacokinetic and potential toxicity properties shows that the two newly predicted compounds T1 and T3 presented a better ADMET profile, in particular a good gastrointestinal absorption, compared to the most active compound taken from the literature,

The anti-oxidant activities for a diverse set of flavonoids as TEAC (Trolox equivalent anti-oxidant capacity), assay were subjected to 3D-QSAR (3 dimensional quantitative structural-activity relationship) studies using CoMFA (comparative molecular field analysis) and CoMSIA (comparative molecular similarity indices analysis). The obtained results indicated superiority of CoMSIA model over CoMFA model. The best CoMSIA model is developed by using hydrogen-bond donor (H-bond donor) and electrostatic field components. This model gave the cross-validated correlation coefficient, Q2 = 0.512, correlation coefficient, R2 = 0.950, standard error of prediction, SE = 0.284, and F = 47.3, for training set, R2 = 0.922 and SE = 0.286, for test set indicating robustness and high prediction power of the developed model. The contour maps of electrostatic and H-bond donor fields of CoMSIA model provide interpretable and fruitful relationship between chemicals structure and their anti-oxidant activities, which give useful insights for designing new compounds with higher activity.