J Chem Inf Model - Combined 3D-QSAR, molecular docking, and molecular dynamics study on piperazinyl-glutamate-pyridines/pyrimidines as potent P2Y12 antagonists for inhibition of platelet aggregation.

Tópicos

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Resumo

An unusually large data set of 397 piperazinyl-glutamate-pyridines/pyrimidines as potent orally bioavailable P2Y(12) antagonists for inhibition of platelet aggregation was studied for the first time based on the combination of three-dimensional quantitative structure-activity relationship (3D-QSAR), molecular docking, and molecular dynamics (MD) methods. The comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) studies have been performed with a training set of 317 compounds, estimating three superimposition methods. The best CoMFA and CoMSIA models, derived from superimposition I, shows leave-one-out cross-validation correlation coefficients (Q(2)) of 0.571 and 0.592 as well as the conventional correlation coefficients (R(2)(ncv)) of 0.814 and 0.834, respectively. In addition, the satisfactory results, based on the bootstrapping analysis and 10-fold cross-validation, further indicate the highly statistical significance of the optimal models. The external predictive abilities of these models were evaluated using a prediction set of 80 compounds, producing the predicted correlation coefficients (R(2)(pred)) of 0.664 and 0.668, respectively. The key amino acid residues were identified by molecular docking, and the stability and rationality of the derived molecular conformations were also validated by MD simulation. The good concordance between the docking results and CoMFA/CoMSIA contour maps provides helpful clues about the rational modification of molecules in order to design more potent P2Y(12) antagonists. We hope the developed models could provide some instructions for further synthesis of highly potent P2Y(12) antagonists.

Resumo Limpo

unusu larg data set piperazinylglutamatepyridinespyrimidin potent oral bioavail py antagonist inhibit platelet aggreg studi first time base combin threedimension quantit structureact relationship dqsar molecular dock molecular dynam md method compar molecular field analysi comfa compar molecular similar index analysi comsia studi perform train set compound estim three superimposit method best comfa comsia model deriv superimposit show leaveoneout crossvalid correl coeffici q well convent correl coeffici rncv respect addit satisfactori result base bootstrap analysi fold crossvalid indic high statist signific optim model extern predict abil model evalu use predict set compound produc predict correl coeffici rpred respect key amino acid residu identifi molecular dock stabil ration deriv molecular conform also valid md simul good concord dock result comfacomsia contour map provid help clue ration modif molecul order design potent py antagonist hope develop model provid instruct synthesi high potent py antagonist

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