Prediction of ion channel activity using binary kernel discrimination.
Voltage-gated ion channels are a diverse family of pharmaceutically important membrane proteins for which limited 3D information is available. A number of virtual screening tools have been used to assist with the discovery of new leads and with the analysis of screening results. One such tool, and the subject of this paper, is binary kernel discrimination (BKD), a machine-learning approach that has recently been applied to applications in chemoinformatics. It uses a training set of compounds, for which both structural and qualitative activity data are known, to produce a model that can then be used to rank another set of compounds in order of likely activity. Here, we report the use of BKD to build models for the prediction of five different ion channel targets using two types of activity data. The results obtained suggest that the approach provides an effective way of prioritizing compounds for acquisition and testing.
Willett
Peter
P
Department of Information Studies, University of Sheffield, 211 Portobello Street, Sheffield S1 4DP, United Kingdom. p.willett@sheffield.ac.uk
Wilton
David
D
Hartzoulakis
Basil
B
Tang
Raymond
R
Ford
John
J
Madge
David
D
D000465:Algorithms; D001185:Artificial Intelligence; D019295:Computational Biology; D003198:Computer Simulation; D004353:Drug Evaluation, Preclinical; D004594:Electrophysiology; D015640:Ion Channel Gating; D007473:Ion Channels / Q000737:chemistry* / Q000378:metabolism*; D008956:Models, Chemical; D026902:Potassium Channel Blockers / Q000737:chemistry / Q000494:pharmacology; D015203:Reproducibility of Results; D026941:Sodium Channel Blockers / Q000737:chemistry / Q000494:pharmacology
D007473:Ion Channels; D026902:Potassium Channel Blockers; D026941:Sodium Channel Blockers