Influence of Varying Training Set Composition and Size on Support Vector Machine-Based Prediction of Active Compounds.

ABSTRACT: Support vector machine (SVM) modeling is one of the most popular machine learning approaches in chemoinformatics and drug design. The influence of training set composition and size on predictions currently is an underinvestigated issue in SVM modeling. In this study, we have derived SVM classification and ranking models for a variety of compound activity classes under systematic variation of the number of positive and negative training examples. With increasing numbers of negative training compounds, SVM classification calculations became increasingly accurate and stable. However, this was only the case if a required threshold of positive training examples was also reached. In addition, consideration of class weights and optimization of cost factors substantially aided in balancing the cal

SUBMITTER: Rodriguez-Perez R 

PROVIDER: S-EPMC5417594 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature