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首页> 外文期刊>Structural Chemistry >In silico studies of novel scaffold of thiazolidin-4-one derivatives as anti-Toxoplasma gondii agents by 2D/3D-QSAR, molecular docking, and molecular dynamics simulations
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In silico studies of novel scaffold of thiazolidin-4-one derivatives as anti-Toxoplasma gondii agents by 2D/3D-QSAR, molecular docking, and molecular dynamics simulations

机译:在噻唑胺-4-一代衍生物的新型支架中的基石研究,作为抗致毒素的琼脂腺症,分子对接和分子动力学模拟

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摘要

Toxoplasma gondii is an obligate intracellular protozoa that can infect a wide variety of warm-blooded animals and humans. It was claimed that novel anti-Toxoplasma gondii agents were optimized as potential drug candidates, designed and created as significant agents. In this work, molecular modeling studies, including CoMFA, CoMFA-RF, CoMSIA, and HQSAR were performed on a set of 59 thiazolidin-4-one derivatives as anti-T. gondii agents. The statistical qualities of generating models were justified by internal and external validation, i.e., cross-validated correlation coefficient (q(2)), non-cross-validated correlation coefficient (rncv2and predicted correlation coefficient (rpred2 respectively. The CoMFA (q(2), 0.897;rncv2 0.933; rpred2 0.938), CoMFA-RF (q(2), 0.900;rncv20.935; rpred2 0.998), CoMSIA (q(2), 0.910;rncv2.950; rpred2 0.998), and HQSAR models (q(2), 0.924;rncv20.953; rpred2, 0.995) for training and test set yielded significant statistical results. Therefore, these QSAR models were excellent, robust, and had better predictive capability. Contour maps of the QSAR models were generated and validated by molecular dynamics simulation-assisted molecular docking study. The final QSAR models could be useful for the design and development of novel potent anti-T. gondii agents.
机译:Toxoplasma Gondii是一种迫使细胞内原生动物,可感染各种温血动物和人类。据称,新型抗弓形虫剂因子被优化为潜在的药物候选者,设计和创造为重要的药剂。在这项工作中,在一组59噻唑烷-4-一种衍生物作为抗-t,对包括COMFA,COMFA-RF,COMSIA和HQSAR进行的分子建模研究。 Gondii代理商。通过内部和外部验证,即交叉验证的相关系数(Q(2)),非交叉验证相关系数(RNCV2和预测相关系数(分别为Q(Q(q(2) ),0.897; RNCV2 0.933; RPRED2 0.938),COMFA-RF(Q(2),0.900; RMCV20.935; RPRED2 0.998),COMSIA(Q(2),0.910; RMCV2.950; RPRED2 0.998)和HQSAR模型(Q(2),0.924; rncv20.953;用于训练和测试集的Rpred2,0.995)产生了显着的统计结果。因此,这些QSAR模型具有优异的,强大,具有更好的预测能力。生成了QSAR模型的轮廓图通过分子动力学仿真辅助分子对接研究验证。最终的QSAR模型对于新颖强度抗-T的设计和开发有用。Gondii代理商。

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