Introduction: 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.

Areas covered: 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.

Expert opinion: 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.