Docking-based CoMFA and CoMSIA studies on naphthyl-substituted diarylpyrimidines as NNRTIs
Docking-based CoMFA and CoMSIA studies on naphthyl-substituted diarylpyrimidines as NNRTIs
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基于对接的 CoMFA 和 CoMSIA 研究萘基取代的二芳基嘧啶作为 NNRTI
DOI：
10.1080/1062936x.2014.955054
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发表时间：
2014-10
影响因子：
3
通讯作者：
Fener Chen
中科院分区：
文献类型：
--
作者：
Haiqiu Wu;Jin Yao;Qiuqin He;Fener Chen
非核苷类逆转录酶抑制剂（NNRTI）在抗HIV药物开发中发挥着重要作用。我们实验室报道了一系列具有纳摩尔范围内大多数EC 50值的萘基取代的二芳基嘧啶作为有效的NNRTI。为了获得能够指导合理先导化合物优化的定量构效关系（QSAR），进行了CoMFA和CoMSIA研究。基于共结晶复合物（PDB ID：3MEC）的对接研究被用作获得用于分子对准的可靠构象的方法。进行了两种不同的分子比对，得到两个CoMFA模型和34个CoMSIA模型。CoMSIA模型对应于空间场、静电场、疏水场、氢键供体和氢键受体五个场之间的所有可能组合。实现了高度预测模型，其中统计可靠的CoMFA模型的q2为0.743，r2为0.980，而最佳CoMSIA模型的q2为0.713，r2为0.969。最好的模型进行了严格的验证与外部测试集，这给出了令人满意的预测r2值的CoMFA和CoMSIA模型：0.85和0.83，分别。从选定的模型获得的等高线图显示了重要的结构特征和进一步优化的一些合理的指导。
Non-nucleoside reverse transcriptase inhibitors (NNRTIs) play a significant role in anti-HIV drug development. A series of naphthyl-substituted diarylpyrimidines with most EC50 values in the nanomolar range was reported as potent NNRTIs by our lab. In order to obtain the quantitative structure–activity relationship (QSAR) that can guide rational lead optimization, CoMFA and CoMSIA studies were carried out. Docking study based on the co-crystallized complex (PDB ID: 3MEC) was utilized as an approach to obtain reliable conformations for molecular alignment. Two different molecular alignments were performed, resulting in two CoMFA models and 34 CoMSIA models. The CoMSIA models correspond to all the possible combinations among five fields: steric, electrostatic, hydrophobic, hydrogen bond donor, and hydrogen bond acceptor. Highly predictive models were achieved, in which the statistically reliable CoMFA model had a q2 of 0.743 and an r2 of 0.980, whereas the best CoMSIA model had a q2 of 0.713 and an r2 of 0.969. The best models were rigorously validated with an external test set, which gave satisfactory predictive r2 values for CoMFA and CoMSIA models: 0.85 and 0.83, respectively. Contour maps obtained from selected models revealed important structural features and some rational guidance for further optimization.
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影响因子：
6.7
作者：
Cichero, Elena;Cesarini, Sara;Mosti, Luisa
通讯作者：
Mosti, Luisa
影响因子：
3.4
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Xiao-qing Feng;Yong‐Hong Liang;Zhao‐sen Zeng;Fener Chen;J. Balzarini;C. Pannecouque;Erik de Clercq-Erik-de Clercq-1693707848
通讯作者：
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7.3
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Böhm, M;Stürzebecher, J;Klebe, G
通讯作者：
Klebe, G