title: 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.
authors.affiliation: Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstr. 2, D-53113 Bonn, Germany.
authors.forename: Kathrin
authors.initials: K
authors.lastname: Heikamp
authors.forename: Jürgen
authors.initials: J
authors.lastname: Bajorath
mesh_terms: D065607:Cytochrome P-450 Enzyme Inhibitors; D003577:Cytochrome P-450 Enzyme System / Q000737:chemistry*; D004791:Enzyme Inhibitors / Q000737:chemistry*; D006801:Humans; D008024:Ligands; D015233:Models, Statistical; D010088:Oxidoreductases / Q000037:antagonists & inhibitors / Q000737:chemistry*; D011237:Predictive Value of Tests; D021281:Quantitative Structure-Activity Relationship; D054852:Small Molecule Libraries / Q000737:chemistry*; D060388:Support Vector Machine
chemical_list: D065607:Cytochrome P-450 Enzyme Inhibitors; D004791:Enzyme Inhibitors; D008024:Ligands; D054852:Small Molecule Libraries; D003577:Cytochrome P-450 Enzyme System; D010088:Oxidoreductases