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首页> 外文期刊>European Journal of Medicinal Chemistry: Chimie Therapeutique >Understanding the antitumor activity of novel tricyclicpiperazinyl derivatives as farnesyltransferase inhibitors using CoMFA and CoMSIA.
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Understanding the antitumor activity of novel tricyclicpiperazinyl derivatives as farnesyltransferase inhibitors using CoMFA and CoMSIA.

机译：使用CoMFA和CoMSIA了解作为法尼基转移酶抑制剂的新型三环哌嗪基衍生物的抗肿瘤活性。

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摘要

3D-QSAR studies of some tricyclicpiperazinyl derivatives as farnesyltransferase inhibitors were performed by comparative molecular field analysis (CoMFA) and comparative molecular similarity indices (CoMSIA) methods to rationalize the structural requirements responsible for the inhibitory activity of these compounds. The global minimum energy conformer of the template molecule 35, the most active and pharmacokinetically stable molecule of the series, was obtained by simulated annealing method and used to build structures of the molecules in the dataset. The CoMFA model obtained after the removal of outliers produced statistically significant results with cross-validated and conventional correlation coefficients of 0.550 and 0.969, respectively. The combination of steric, electrostatic, hydrogen bond acceptor and hydrophobic fields in CoMSIA gave the best results with cross-validated and conventional correlation coefficients of 0.611 and 0.986, respectively. The predictive ability of CoMFA and CoMSIA were determined using a test set of 24 tricyclicpiperazinyl derivatives giving predictive correlation coefficients of 0.543 and 0.663, respectively, indicating good predictive power. Further the robustness of the model was verified by bootstrapping analysis. Based on the CoMFA and CoMSIA analysis we have identified some key features in the tricyclicpiperazinyl series that are responsible for farnesyltransferase inhibitory activity that may be used to design more potent tricyclicpiperazinyl derivatives and predict their activity prior to synthesis.
机译：通过比较分子场分析（CoMFA）和比较分子相似性指数（CoMSIA）方法对某些三环哌嗪基衍生物作为法呢基转移酶抑制剂进行了3D-QSAR研究，以合理化这些化合物抑制活性的结构要求。通过模拟退火方法获得了模板分子35（该系列中活性最高，药代动力学最稳定的分子）的全局最小能量构象异构体，并将其用于构建数据集中的分子结构。去除异常值后获得的CoMFA模型产生了具有统计学意义的结果，交叉验证的和传统的相关系数分别为0.550和0.969。 CoMSIA中空间，静电，氢键受体和疏水域的组合给出了最佳结果，交叉验证的和传统的相关系数分别为0.611和0.986。使用24种三环哌嗪基衍生物的测试集确定CoMFA和CoMSIA的预测能力，其预测相关系数分别为0.543和0.663，表明具有良好的预测能力。通过自举分析进一步验证了模型的鲁棒性。基于CoMFA和CoMSIA分析，我们确定了三环哌嗪基系列中的一些关键特征，这些特征可导致法呢基转移酶抑制活性，可用于设计更有效的三环哌嗪基衍生物并在合成前预测其活性。

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