Potency-directed similarity searching using support vector machines.
Support vector machine modeling has become increasingly popular in chemoinformatics. Recently, several advanced support vector machine applications have been reported including, among others, multitask learning for ligand-target prediction. Here, we introduce another support vector machine approach to add compound potency information to similarity searching and enrich database selection sets with potent hits. For this purpose, we introduce a structure-activity kernel function and a potency-oriented support vector machine linear combination approach. Using fingerprint descriptors, potency-directed support vector machine searching has been successfully applied to four high-throughput screening data sets, and different support vector machine strategies have been compared. For potency-balanced compound reference sets, potency-directed support vector machine searching meets or exceeds recall rates of standard support vector machine calculations but detects many more potent hits.
Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität Bonn, Dahlmannstrasse 2, Bonn, Germany.
Anne M
AM
Wassermann
Kathrin
K
Heikamp
Jürgen
J
Bajorath
D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D016208:Databases, Factual; D004353:Drug Evaluation, Preclinical / Q000379:methods; D057166:High-Throughput Screening Assays / Q000379:methods*; D006801:Humans; D020128:Inhibitory Concentration 50; D008024:Ligands; D016014:Linear Models; D013329:Structure-Activity Relationship
D008024:Ligands