Literature DB >> 21923153

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

Ming Hao1, Yan Li, Yonghua Wang, Yulian Yan, Shuwei Zhang, Guohui Li, Ling Yang.   

Abstract

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.

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Year:  2011        PMID: 21923153     DOI: 10.1021/ci2002878

Source DB:  PubMed          Journal:  J Chem Inf Model        ISSN: 1549-9596            Impact factor:   4.956


  4 in total

1.  QSAR model based on weighted MCS trees approach for the representation of molecule data sets.

Authors:  Bernardo Palacios-Bejarano; Gonzalo Cerruela García; Irene Luque Ruiz; Miguel Ángel Gómez-Nieto
Journal:  J Comput Aided Mol Des       Date:  2013-02-06       Impact factor: 3.686

Review 2.  Open-source chemogenomic data-driven algorithms for predicting drug-target interactions.

Authors:  Ming Hao; Stephen H Bryant; Yanli Wang
Journal:  Brief Bioinform       Date:  2019-07-19       Impact factor: 11.622

3.  Atom-based 3D-QSAR, molecular docking and molecular dynamics simulation assessment of inhibitors for thyroid hormone receptor α and β.

Authors:  Manish Kumar Gupta; Krishna Misra
Journal:  J Mol Model       Date:  2014-06-05       Impact factor: 1.810

4.  Toward the prediction of FBPase inhibitory activity using chemoinformatic methods.

Authors:  Ming Hao; Shuwei Zhang; Jieshan Qiu
Journal:  Int J Mol Sci       Date:  2012-06-07       Impact factor: 6.208

  4 in total

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