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首页> 外文期刊>European Journal of Medicinal Chemistry: Chimie Therapeutique >3D-QSAR studies on the inhibitors of AP-1 and NF-kappaB mediated transcriptional activation.
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3D-QSAR studies on the inhibitors of AP-1 and NF-kappaB mediated transcriptional activation.

机译：关于AP-1和NF-κB介导的转录激活抑制剂的3D-QSAR研究。

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摘要

Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were performed on a series of 68 inhibitors of AP-1 and NF-kappaB mediated transcriptional activations. The CoMFA model produced statistically significant results with the cross-validated q(2) of 0.594 and the conventional correlation coefficient r(2) of 0.968. The best CoMSIA model was obtained by the combination use of steric, electrostatic, hydrogen-bond donor and acceptor fields. The corresponding q(2) and r(2) of CoMSIA model were 0.703 and 0.932, respectively. From the cross-validated results, it can be seen that the CoMSIA model has a better predictive ability than CoMFA model due to the importance of the hydrogen bonds for the activity of these inhibitors. The predictive abilities of the two models were further validated by a test set of 15 compounds. The models gave predicted correlation coefficient r(pred)(2) of 0.891 for CoMFA model and 0.810 for CoMSIA model. Based on the above results, we identified the key structural features that may help to design potent inhibitors with improved activities: (1) the NH linker at the position R(4) acts as important hydrogen-bond donor and any group on phenyl or 2-thienyl ring of R(1) substituent decreases inhibitory activity, (2)further structural modification of compound 50 on the phenyl ring of the quinazoline ring considering steric, electrostatic and hydrogen-bond acceptor properties will influence the inhibitory activity.
机译：对一系列68种AP-1和NF-κB介导的转录激活抑制剂进行了比较分子场分析（CoMFA）和比较分子相似性指数分析（CoMSIA）。 CoMFA模型产生了具有统计学意义的结果，其中交叉验证的q（2）为0.594，常规相关系数r（2）为0.968。最佳的CoMSIA模型是通过结合使用空间位，静电场，氢键供体和受体场获得的。 CoMSIA模型的相应q（2）和r（2）分别为0.703和0.932。从交叉验证的结果可以看出，由于氢键对于这些抑制剂活性的重要性，因此CoMSIA模型比CoMFA模型具有更好的预测能力。通过15种化合物的测试集进一步验证了两个模型的预测能力。这些模型的预测相关系数r（pred）（2）对于CoMFA模型为0.891，对于CoMSIA模型为0.810。根据以上结果，我们确定了可能有助于设计具有改善活性的强抑制剂的关键结构特征：（1）R（4）位置的NH接头是重要的氢键供体，苯基或2上的任何基团R（1）取代基的-噻吩环降低了抑制活性，考虑到空间，静电和氢键受体特性，喹唑啉环的苯环上化合物50的进一步结构改性将影响抑制活性。

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