Prediction of Compounds with Closely Related Activity Profiles Using Weighted Support Vector Machine Linear Combinations

Abstract

Using support vector machine (SVM) ranking, a complex multi-class prediction task has been investigated involving sets of compounds that were active against related targets and represented all possible combinations of single-, dual-, and triple-target activities. Standard SVM models were not capable of differentiating compounds with overlapping yet distinct activity profiles. To address this problem, we designed differentially weighted SVM linear combinations that were found to preferentially detect compounds with desired activity profiles and deprioritize others. Hence, combining independently derived SVM models using negative and positive linear weighting factors balanced relative contributions from individual reference sets and successfully distinguished between compounds with overlapping activity profiles.