title: Target specific compound identification using a support vector machine.
abstract: In many cases at the beginning of an HTS-campaign, some information about active molecules is already available. Often known active compounds (such as substrate analogues, natural products, inhibitors of a related protein or ligands published by a pharmaceutical company) are identified in low-throughput validation studies of the biochemical target. In this study we evaluate the effectiveness of a support vector machine applied for those compounds and used to classify a collection with unknown activity. This approach was aimed at reducing the number of compounds to be tested against the given target. Our method predicts the biological activity of chemical compounds based on only the atom pairs (AP) two dimensional topological descriptors. The supervised support vector machine (SVM) method herein is trained on compounds from the MDL drug data report (MDDR) known to be active for specific protein target. For detailed analysis, five different biological targets were selected including cyclooxygenase-2, dihydrofolate reductase, thrombin, HIV-reverse transcriptase and antagonists of the estrogen receptor. The accuracy of compound identification was estimated using the recall and precision values. The sensitivities for all protein targets exceeded 80% and the classification performance reached 100% for selected targets. In another application of the method, we addressed the absence of an initial set of active compounds for a selected protein target at the beginning of an HTS-campaign. In such a case, virtual high-throughput screening (vHTS) is usually applied by using a flexible docking procedure. However, the vHTS experiment typically contains a large percentage of false positives that should be verified by costly and time-consuming experimental follow-up assays. The subsequent use of our machine learning method was found to improve the speed (since the docking procedure was not required for all compounds from the database) and also the accuracy of the HTS hit lists (the enrichment factor).
authors.affiliation: BioInfoBank Institute, Limanowskiego 24A/16, 60-744 Poznan, Poland. darman@bioinfo.pl
authors.forename: Dariusz
authors.initials: D
authors.lastname: Plewczynski
authors.forename: Marcin
authors.initials: M
authors.lastname: von Grotthuss
authors.forename: Stephane A H
authors.initials: SA
authors.lastname: Spieser
authors.forename: Leszek
authors.initials: L
authors.lastname: Rychlewski
authors.forename: Lucjan S
authors.initials: LS
authors.lastname: Wyrwicz
authors.forename: Krzysztof
authors.initials: K
authors.lastname: Ginalski
authors.forename: Uwe
authors.initials: U
authors.lastname: Koch
mesh_terms: D001185:Artificial Intelligence; D016503:Drug Delivery Systems / Q000379:methods*; D004353:Drug Evaluation, Preclinical / Q000379:methods*; D004791:Enzyme Inhibitors; D006801:Humans; D011485:Protein Binding; D021281:Quantitative Structure-Activity Relationship; D011956:Receptors, Cell Surface / Q000037:antagonists & inhibitors
chemical_list: D004791:Enzyme Inhibitors; D011956:Receptors, Cell Surface