Rheinische Friedrich-Wilhelms-Universität, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Department of Life Science Informatics, Dahlmannstr 2, D-53113 Bonn , Germany +49 228 2699 306 ; +49 228 2699 341 ;
Expert Opin Drug Discov. 2014 Jan;9(1):93-104. doi: 10.1517/17460441.2014.866943. Epub 2013 Dec 5.
Support vector machines (SVMs) are supervised machine learning algorithms for binary class label prediction and regression-based prediction of property values. In recent years, SVMs have become increasingly popular for drug discovery-relevant applications such as compound classification, the search for novel active compounds and property predictions.
The authors provide the readers with a brief introduction of SVM theory and discuss the kernel functions designed for drug discovery applications. The authors also review the different types of SVM applications in drug discovery, looking at particular case studies. Furthermore, the authors discuss the recent hybrid methods developed that incorporate SVM modeling in different ways.
SVMs are currently among the best-performing approaches for chemical and biological property prediction and the computational identification of active compounds. It is anticipated that their use in drug discovery will further increase. Indeed, this will also include the development of SVM-based meta-classifiers that combine different approaches and exploit their individual strengths and complementarity.
支持向量机（SVMs）是用于二进制类标签预测和基于回归的属性值预测的监督机器学习算法。近年来，SVM 在药物发现相关应用中越来越受欢迎，例如化合物分类、寻找新型活性化合物和属性预测。
作者为读者提供了 SVM 理论的简要介绍，并讨论了专为药物发现应用设计的核函数。作者还回顾了不同类型的 SVM 在药物发现中的应用，着眼于特定的案例研究。此外，作者还讨论了最近开发的混合方法，这些方法以不同的方式将 SVM 建模纳入其中。
SVM 目前是化学和生物性质预测以及活性化合物计算识别方面表现最好的方法之一。预计它们在药物发现中的应用将进一步增加。实际上，这还将包括开发基于 SVM 的元分类器，这些分类器可以结合不同的方法，并利用它们各自的优势和互补性。
