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

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.

Constructed models achieved 95% and 90% accuracy in classifying HERG actives and inactives as a result of 10-fold cross validation. The two test sets consist of 73 diverse drugs.Figure optionsDownload as PowerPoint slide