摘要
利用分子全息技术研究了129个5-羧基苯并咪唑类HCV NS5B聚合酶抑制剂的结构与活性之间的关系.讨论了分子碎片大小、碎片区分参数及全息长度对模型质量的影响.利用偏最小二乘法(partial least square,PLS)建立了一组以99个化合物为训练集的最优模型,该模型的交叉验证相关系数q^2=0.820,非交叉验证相关系数r^2=0.963,标准偏差SEE=0.213;用最优模型对由30个化合物组成的测试集进行预测,得到其相关系数r_(pred)~2=0.98,表明了该模型具有良好的预测能力及拟合能力.利用色码图对模型中不同原子及不同结构的贡献进行了解释,在此基础上根据最优HQSAR模型设计了几种具有良好抗HCV活性的苯并咪唑类HCV NS5B聚合酶抑制剂分子,为新型HCV NS5B聚合酶抑制剂的设计和优化提供了参考.
The structure-activity relationship of 129 benzimidazole 5-carboxylic derivatives of hepatitis C virus NS5 B polymerase inhibitors were investigated using hologram quantitative structure-activity relationship(HQSAR)techniques.The model parameters studied include:influences of molecular fragment,fragment distinction and molecular holographic length.In order to establish the optimal model,99 compounds have been used to composed the training set.Results obtained from this set were analyzed by the partial least square(PLS),with the cross-validated q^2=0.820,convention r^2=0.963 and standard deviation SEE=0.213.The model was then further validated with a test set of 30 compounds.The predicted values were in good agreement with the experimental results(rpred^2=0.98),showing that the model has potential ability in structural predicting and property fitting.The contributions of different atoms to activity were able to indicated with color coding figures.Based on this HQSAR model,a series of 11 benzimidazole 5-carboxylic derivatives with anti-HCV inhibition properties were designed.This model could provide additional tool in HCV NS5 B polymerase inhibitor designing and optimizing.
作者
王子恒
陈娴
张玉芳
王月平
何严萍
Ziheng Wang Xian Chen Yufang Zhang Yueping Wang Yanping He(Key Laboratory of Medicinal Chemistry for Natural Resource, Ministry of Education, School of Chemical Science and Technology/College of Pharmacy, Yunnan University, Kunming 650091, China Department of Applied Chemistry, Faculty of Science, Kunming University of Science and Technology, Kunming 650050, China)
出处
《中国科学:化学》
CAS
CSCD
北大核心
2017年第3期350-360,共11页
SCIENTIA SINICA Chimica
基金
国家自然科学基金(编号:21262044,21362017)资助项目