...
首页> 外文期刊>Journal of molecular graphics & modelling >3D-QSAR and molecular docking studies of selective agonists for the thyroid hormone receptor beta
【24h】

3D-QSAR and molecular docking studies of selective agonists for the thyroid hormone receptor beta

机译:甲状腺激素受体β选择性激动剂的3D-QSAR和分子对接研究

获取原文
获取原文并翻译 | 示例
   

获取外文期刊封面封底 >>

       

摘要

Three-dimensional quantitative structure-activity relationship (3D-QSAR) models were developed using comparative molecular field analysis (CoMFA) and comparative molecular similarity analysis (CoMSIA) on a series of agonists of thyroid hormone receptor beta (TR beta), which may lead to safe therapies for non-thyroid disorders while avoiding the cardiac side effects. The reasonable q(2) (cross-validated) values 0.600 and 0.616 and non-cross-validated r(2) values of 0.974 and 0.974 were obtained for CoMFA and CoMSIA models for the training set compounds, respectively. The predictive ability of two models was validated using a test set of 12 molecules which gave predictive correlation coefficients (r(pred)(2)) of 0.688 and 0.674, respectively. The Lamarckian Genetic Algorithm (LGA) of AutoDock 4.0 was employed to explore the binding mode of the compound at the active site of TR beta. The results not only lead to a better understanding of interactions between these agonists and the thyroid hormone receptor P but also can provide us some useful information about the influence of structures on the activity which will be very useful for designing some new agonist with desired activity. (C) 2008 Elsevier Inc. All rights reserved.
机译:使用比较分子场分析(CoMFA)和比较分子相似性分析(CoMSIA)对一系列甲状腺激素受体β(TR beta)激动剂开发了三维定量构效关系(3D-QSAR)模型安全治疗非甲状腺疾病,同时避免心脏副作用。对于训练集化合物的CoMFA和CoMSIA模型,分别获得了合理的q(2)(交叉验证)值0.600和0.616和非交叉验证r(2)值0.974和0.974。使用12个分子的测试集验证了两个模型的预测能力,该测试集给出的预测相关系数(r(pred)(2))分别为0.688和0.674。采用AutoDock 4.0的Lamarckian遗传算法(LGA)探索化合物在TR beta活性位点的结合模式。结果不仅使人们更好地了解了这些激动剂与甲状腺激素受体P之间的相互作用,而且还可以为我们提供有关结构对活性影响的有用信息,这对于设计具有所需活性的新型激动剂非常有用。 (C)2008 Elsevier Inc.保留所有权利。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号