title: A discriminant model constructed by the support vector machine method for HERG potassium channel inhibitors.
abstract: HERG attracts attention as a risk factor for arrhythmia, which might trigger torsade de pointes. A highly accurate classifier of chemical compounds for inhibition of the HERG potassium channel is constructed using support vector machine. For two test sets, our discriminant models achieved 90% and 95% accuracy, respectively. The classifier is even applied for the prediction of cardio vascular adverse effects to achieve about 70% accuracy. While modest inhibitors are partly characterized by properties linked to global structure of a molecule including hydrophobicity and diameter, strong inhibitors are exclusively characterized by properties linked to substructures of a molecule.
authors.affiliation: Reverse proteomics research institute, Kisarazu-si, Chiba, Japan. toby@rd.hitachi.co.jp
authors.forename: Motoi
authors.initials: M
authors.lastname: Tobita
authors.forename: Tetsuo
authors.initials: T
authors.lastname: Nishikawa
authors.forename: Renpei
authors.initials: R
authors.lastname: Nagashima
mesh_terms: D000818:Animals; D016466:CHO Cells; D006224:Cricetinae; D016002:Discriminant Analysis; D000072237:ERG1 Potassium Channel; D051638:Ether-A-Go-Go Potassium Channels; D006801:Humans; D026902:Potassium Channel Blockers / Q000494:pharmacology*; D024642:Potassium Channels, Voltage-Gated / Q000037:antagonists & inhibitors*
chemical_list: D000072237:ERG1 Potassium Channel; D051638:Ether-A-Go-Go Potassium Channels; C000606913:KCNH2 protein, human; D026902:Potassium Channel Blockers; D024642:Potassium Channels, Voltage-Gated