Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Friedrich-Hirzebruch-Allee 6, D-53115, Bonn, Germany.
Novartis Institutes for Biomedical Research, Novartis Campus, CH-4002, Basel, Switzerland.
J Comput Aided Mol Des. 2022 May;36(5):355-362. doi: 10.1007/s10822-022-00442-9. Epub 2022 Mar 19.
The support vector machine (SVM) algorithm is one of the most widely used machine learning (ML) methods for predicting active compounds and molecular properties. In chemoinformatics and drug discovery, SVM has been a state-of-the-art ML approach for more than a decade. A unique attribute of SVM is that it operates in feature spaces of increasing dimensionality. Hence, SVM conceptually departs from the paradigm of low dimensionality that applies to many other methods for chemical space navigation. The SVM approach is applicable to compound classification, and ranking, multi-class predictions, and -in algorithmically modified form- regression modeling. In the emerging era of deep learning (DL), SVM retains its relevance as one of the premier ML methods in chemoinformatics, for reasons discussed herein. We describe the SVM methodology including strengths and weaknesses and discuss selected applications that have contributed to the evolution of SVM as a premier approach for compound classification, property predictions, and virtual compound screening.
支持向量机（SVM）算法是用于预测活性化合物和分子性质的最广泛使用的机器学习（ML）方法之一。在化学信息学和药物发现中，SVM 已经成为十年来最先进的 ML 方法之一。SVM 的一个独特属性是它在不断增加维度的特征空间中运行。因此，SVM 在概念上偏离了适用于许多其他化学空间导航方法的低维范式。SVM 方法适用于化合物分类、排序、多类预测以及-在算法上修改的形式-回归建模。在深度学习（DL）的新兴时代，SVM 仍然是化学信息学中最重要的 ML 方法之一，原因在此文中进行了讨论。我们描述了 SVM 方法学，包括其优缺点，并讨论了一些选定的应用，这些应用促进了 SVM 作为化合物分类、性质预测和虚拟化合物筛选的主要方法的发展。
