Evolution of Support Vector Machine and Regression Modeling in Chemoinformatics and Drug Discovery

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.

Automated summary

Support Vector Machine (SVM) algorithm, as one of the most widely used machine learning methods, has been applied in predicting active compounds and molecular properties for over a decade. It operates in feature spaces of increasing dimensionality, distinguishing itself from many other methods. SVM is applicable to compound classification, ranking, multi-class predictions, and regression modeling. It remains relevant in the emerging era of deep learning and stands as one of the premier ML methods in chemoinformatics.