Hasegawa Kiyoshi, Funatsu Kimito
Chugai Pharmaceutical Company, Kamakura Research Laboratories, Kajiwara 200, Kamakura, Kanagawa 247-8530, Japan.
Curr Comput Aided Drug Des. 2010 Mar;6(1):24-36. doi: 10.2174/157340910790980124.
In quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR), there is a considerable interest in support vector machine (SVM) and support vector regression (SVR) for data modeling. SVM and SVR have a high performance for classification and regression rates, but their chemical interpretations are not feasible. In this review, we present some promising approaches to visualize and interpret the SVM and SVR models. This type analysis would be useful for molecular design. Representative examples derived from chemoinformatics and bioinformatics are highlighted in detail. We also refer to a structure generator based on SVR score in the framework of de novo design. Furthermore, we provide readers the theoretical description of SVM and SVR.
在定量构效关系（QSAR）和定量构性关系（QSPR）中，支持向量机（SVM）和支持向量回归（SVR）在数据建模方面备受关注。SVM和SVR在分类和回归率方面具有高性能，但它们的化学解释并不可行。在本综述中，我们提出了一些有前景的方法来可视化和解释SVM和SVR模型。这种类型的分析对于分子设计将是有用的。详细突出了源自化学信息学和生物信息学的代表性实例。我们还在从头设计框架中提及了基于SVR分数的结构生成器。此外，我们为读者提供了SVM和SVR的理论描述。
