Modeling of compound profiling experiments using support vector machines.
Profiling of compounds against target families has become an important approach in pharmaceutical research for the identification of hits and analysis of selectivity and promiscuity patterns. We report on modeling of profiling experiments involving 429 potential inhibitors and a panel of 24 different kinases using support vector machine (SVM) techniques and naïve Bayesian classification. The experimental matrix contained many different activity profiles. SVM predictions achieved overall high accuracy due to consistently low false-positive and consistently high true-negative rates. However, predictions for promiscuous inhibitors were affected by false-negative rates. Combined target-based SVM classifiers reached or exceeded the performance of SVM profile prediction methods and were superior to Bayesian classification. The classifiers displayed different prediction characteristics including diverse combinations of false-positive and true-negative rates. Predicted and experimentally observed compound activity profiles were compared in detail, revealing activity patterns modeled with different accuracy.
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.
Jenny
J
Balfer
Kathrin
K
Heikamp
Stefan
S
Laufer
Jürgen
J
Bajorath
D000818:Animals; D001499:Bayes Theorem; D017076:Computer-Aided Design; D055808:Drug Discovery / Q000379:methods*; D006801:Humans; D047428:Protein Kinase Inhibitors / Q000737:chemistry* / Q000494:pharmacology*; D011494:Protein Kinases / Q000378:metabolism; D060388:Support Vector Machine
D047428:Protein Kinase Inhibitors; D011494:Protein Kinases
Bayesian classification; activity profile prediction; compound profiling; inhibitors; machine learning; protein kinases; support vector machines; target families