National Cancer Institute, National Institutes of Health, 376 Boyles Street, Frederick, MD 21702, USA.
Chem Res Toxicol. 2012 Nov 19;25(11):2378-85. doi: 10.1021/tx300247r. Epub 2012 Nov 2.
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 (IC(50), 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(2)(test) = 0.6-0.9; three tests sets had lower R(2)(test) 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 silico prediction of 32 antitarget end-points: http://www.pharmaexpert.ru/GUSAR/antitargets.html.
评估化合物与抗靶标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，我们描述了基于多层次和定量原子描述符邻域以及自洽回归的抗靶标终点 QSAR 模型的开发和验证。使用了与 18 种抗靶标蛋白（13 种受体、2 种酶和 3 种转运蛋白）相互作用的 4000 种化学化合物的数据来构建 32 组终点（IC(50)、K(i)和 K(act)）的模型。每个集都随机划分为 80%的训练集和 20%的测试集。使用基于训练集创建的 QSAR 模型对测试集进行外部验证。所有测试集的预测覆盖率均超过 95%，其中一半的测试集的预测覆盖率为 100%。基于外部测试集，29 个终点的预测准确性通常在 R(2)(test) = 0.6-0.9 的范围内；有三个测试集的 R(2)(test)值较低，具体为 0.55-0.6。所提出的方法对 91%的抗靶标终点具有合理的预测准确性，并且对所有外部测试集均具有较高的覆盖率。在此基础上，我们开发了一个免费的在线服务，用于 32 种抗靶标终点的计算机预测：http://www.pharmaexpert.ru/GUSAR/antitargets.html。
