Quantitative Prediction of Antitarget Interaction Profiles for Chemical Compounds
Quantitative Prediction of Antitarget Interaction Profiles for Chemical Compounds
复制标题
DOI：
10.1021/tx300247r
复制
发表时间：
2012-11-01
影响因子：
4.1
通讯作者：
Poroikov, Vladimir V.
中科院分区：
文献类型：
--
作者：
Zakharov, Alexey V.;Lagunin, Alexey A.;Poroikov, Vladimir V.
评价化合物与抗靶蛋白之间可能的相互作用是研究和开发过程中的一项重要任务。在这里，我们描述了用于预测反目标端点的QSAR模型的开发和验证，该模型是基于原子描述符的多层次和定量邻域和自洽回归创建的。利用4000种化合物与18种抗靶蛋白（13种受体、2种酶和3种转运蛋白）相互作用的数据，建立了32组终点（IC50、K-i和K-act）模型。每个集随机分为训练集和测试集，比例分别为80%和20%。测试集用于在训练集基础上创建的QSAR模型的外部验证。所有测试集的预测覆盖率都超过95%，其中一半测试集的预测覆盖率达到100%。基于外部测试集的29个终点的预测准确度通常在R-test(2) = 0.6-0.9的范围内；三个检验集的r检验(2)值较低，为0.55 ~ 0.6。所提出的方法对91%的反目标端点的预测精度合理，并且对所有外部测试集的覆盖率很高。在创建的模型的基础上，我们开发了一个免费的在线服务，用于预测32个反靶标端点：http://www.pharmaexpert.ru/GUSAR/antitargets.html。
The evaluation of possible interactions between chemical compounds and antitarget proteins is an important task of the research and development process. Here, we describe the development and validation of QSAR models for the prediction of antitarget end-points, created on the basis of multilevel and quantitative neighborhoods of atom descriptors and self-consistent regression. Data on 4000 chemical compounds interacting with 18 antitarget proteins (13 receptors, 2 enzymes, and 3 transporters) were used to model 32 sets of end-points (IC50, K-i, and K-act). Each set was randomly divided into training and test sets in a ratio of 80% to 20%, respectively. The test sets were used for external validation of QSAR models created on the basis of the training sets. The coverage of prediction for all test sets exceeded 95%, and for half of the test sets, it was 100%. The accuracy of prediction for 29 of the end-points, based on the external test sets, was typically in the range of R-test(2) = 0.6-0.9; three tests sets had lower R-test(2) values, specifically 0.55-0.6. The proposed approach showed a reasonable accuracy of prediction for 91% of the antitarget end-points and high coverage for all external test sets. On the basis of the created models, we have developed a freely available online service for in silica prediction of 32 antitarget end-points: http://www.pharmaexpert.ru/GUSAR/antitargets.html.