Rational selection of training and test sets for the development of validated QSAR models
Rational selection of training and test sets for the development of validated QSAR models
复制标题
DOI：
10.1023/a:1025386326946
复制
发表时间：
2003-02-01
影响因子：
3.5
通讯作者：
Tropsha, A
中科院分区：
文献类型：
--
作者：
Golbraikh, A;Shen, M;Tropsha, A
定量构效关系（QSAR）模型越来越多地用于筛选化学数据库和/或虚拟化学文库中潜在的生物活性分子。这些发展强调了严格的模型验证的重要性，以确保模型具有可接受的预测能力。使用k最近邻（kNN）变量选择QSAR方法对多个数据集进行分析，我们最近证明了广泛接受的留一（LOO）交叉验证的R-2 （q(2)）是评估模型预测能力的不足特征[Golbraikh， A., Tropsha， A.注意q2！[j].图形学报，2002,26(2):669 - 676。在此，我们提供了额外的证据，证明训练集的q(2)值与测试集的预测精度（R-2）之间不存在相关性，并认为这一观察结果是使用LOO交叉验证开发的任何QSAR模型的一般性质。我们建议合理选择训练集和测试集进行外部验证，为建立可靠的QSAR模型提供了一种手段。我们提出了几种将实验数据集划分为训练集和测试集的方法，并将其应用于48种功能化氨基酸抗惊厥药和157种具有抗肿瘤活性的表臼毒素衍生物的QSAR研究。我们制定了一套评估QSAR模型预测能力的一般标准。
Quantitative Structure-Activity Relationship (QSAR) models are used increasingly to screen chemical databases and/or virtual chemical libraries for potentially bioactive molecules. These developments emphasize the importance of rigorous model validation to ensure that the models have acceptable predictive power. Using k nearest neighbors (kNN) variable selection QSAR method for the analysis of several datasets, we have demonstrated recently that the widely accepted leave-one-out (LOO) cross-validated R-2 (q(2)) is an inadequate characteristic to assess the predictive ability of the models [Golbraikh, A., Tropsha, A. Beware of q2! J. Mol. Graphics Mod. 20, 269-276, (2002)]. Herein, we provide additional evidence that there exists no correlation between the values of q(2) for the training set and accuracy of prediction (R-2) for the test set and argue that this observation is a general property of any QSAR model developed with LOO cross-validation. We suggest that external validation using rationally selected training and test sets provides a means to establish a reliable QSAR model. We propose several approaches to the division of experimental datasets into training and test sets and apply them in QSAR studies of 48 functionalized amino acid anticonvulsants and a series of 157 epipodophyllotoxin derivatives with antitumor activity. We formulate a set of general criteria for the evaluation of predictive power of QSAR models.