Gramatica Paola
QSAR Research Unit in Environmental Chemistry and Ecotoxicology, Theoretical and Applied Sciences, University of Insubria, via Dunant 3, Varese, Italy.
Methods Mol Biol. 2013;930:499-526. doi: 10.1007/978-1-62703-059-5_21.
The fundamental and more critical steps that are necessary for the development and validation of QSAR models are presented in this chapter as best practices in the field. These procedures are discussed in the context of predictive QSAR modelling that is focused on achieving models of the highest statistical quality and with external predictive power. The most important and most used statistical parameters needed to verify the real performances of QSAR models (of both linear regression and classification) are presented. Special emphasis is placed on the validation of models, both internally and externally, as well as on the need to define model applicability domains, which should be done when models are employed for the prediction of new external compounds.
本章介绍了开发和验证定量构效关系(QSAR)模型所需的基本且更关键的步骤,作为该领域的最佳实践。这些程序是在预测性QSAR建模的背景下进行讨论的,该建模侧重于实现具有最高统计质量和外部预测能力的模型。文中介绍了验证QSAR模型(包括线性回归和分类模型)实际性能所需的最重要且最常用的统计参数。特别强调了模型的内部和外部验证,以及定义模型适用域的必要性,当使用模型预测新的外部化合物时应进行此操作。