首页> 外文期刊>European Journal of Medicinal Chemistry: Chimie Therapeutique >CoMFA and docking studies of 2-phenylindole derivatives with anticancer activity.
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CoMFA and docking studies of 2-phenylindole derivatives with anticancer activity.

机译:CoMFA和具有抗癌活性的2-苯基吲哚衍生物的对接研究。

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摘要

Three-dimensional (3D) quantitative structure-activity relationship (QSAR) and docking studies of 43 tubulin inhibitors, 2-phenylindole derivatives with anticancer activity against human breast cancer cell line MDA-MB 231, have been carried out. The established 3D-QSAR model from the comparative molecular field analysis (CoMFA) in training set shows not only significant statistical quality, but also satisfying predictive ability, with high correlation coefficient value (R(2)=0.910) and cross-validation coefficient value (q(2)=0.705). Moreover, the predictive ability of the CoMFA model was further confirmed by a test set, giving the predictive correlation coefficient (R(2)(pred)) of 0.688. Based on the CoMFA contour maps and docking analyses, some key structural factors responsible for anticancer activity of this series of compounds were revealed as follows: the substituent R(1) should have higher electronegativity; the substituent R(2) should be linear alkyl with four or five carbon atoms in length; and the substituent R(3) should be selected to OCH(3)-kind group whereas should not be selected to CF(3)-kind group. Meanwhile, the interaction information between target and ligand was presented in detail. Such results can offer some useful theoretical references for understanding the action mechanism, designing more potent inhibitors and predicting their activities prior to synthesis.
机译:已经对43种微管蛋白抑制剂,对人乳腺癌细胞系MDA-MB 231具有抗癌活性的2-苯基吲哚衍生物进行了三维(3D)定量构效关系(QSAR)和对接研究。通过训练集中的比较分子场分析(CoMFA)建立的3D-QSAR模型不仅显示出显着的统计质量,而且还具有令人满意的预测能力,具有高相关系数值(R(2)= 0.910)和交叉验证系数值(q(2)= 0.705)。此外,通过测试集进一步确认了CoMFA模型的预测能力,得出的预测相关系数(R(2)(pred))为0.688。根据CoMFA轮廓图和对接分析,揭示了负责该系列化合物抗癌活性的一些关键结构因素,如下所示:取代基R(1)应具有更高的电负性;取代基R(2)应为具有四个或五个碳原子的直链烷基;取代基R(3)应选择为OCH(3)类,而不应选择为CF(3)类。同时,详细介绍了靶标与配体之间的相互作用信息。这些结果可为理解作用机理,设计更有效的抑制剂并在合成前预测其活性提供一些有用的理论参考。

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