جستجوی مقالات مرتبط با کلیدواژه « CoMSIA » در نشریات گروه « علوم پایه »

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.

A series of 42 Pyrazolo[4,3-h]quinazoline-3-carboxamides as multi-cyclin-dependent kinase inhibitors regarded as promising antitumor agents to complement the existing therapies, was subjected to a three-dimensional quantitative activity relationship (3D QSAR). Different QSAR methods, comparative molecular field analysis (CoMFA), CoMFA region focusing, and comparative molecular similarity indices analysis (CoMSIA), were compared. All these QSARbased models had good statistical parameters and yielded q2 values of 0.717, 0.806, and 0.557, respectively. The CoMFA region focusing model provided the highest q2 and r2 values, which implied the significance of correlation of steric and electrostatic fields with biological activities. The quality of CoMSIA was slightly lower than that of CoMFA region focusing in terms of q2 and r2 values. The results of 3D contour maps can be useful for the future development of CDKs inhibitors. The results of 3D QSAR models are in agreement with docking results, and the statistical parameters of the models explain that the data are well fitted and have high predictive ability.