A discriminant model constructed by the support vector machine method for HERG potassium channel inhibitors

Bioorg Med Chem Lett. 2005 Jun 2;15(11):2886-90. doi: 10.1016/j.bmcl.2005.03.080.


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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Animals
  • CHO Cells
  • Cricetinae
  • Discriminant Analysis
  • ERG1 Potassium Channel
  • Ether-A-Go-Go Potassium Channels
  • Humans
  • Potassium Channel Blockers / pharmacology*
  • Potassium Channels, Voltage-Gated / antagonists & inhibitors*


  • ERG1 Potassium Channel
  • Ether-A-Go-Go Potassium Channels
  • KCNH2 protein, human
  • Potassium Channel Blockers
  • Potassium Channels, Voltage-Gated