A binary QSAR model for classification of hERG potassium channel blockers

Abstract

Acquired long QT syndrome causes severe cardiac side effects and represents a major problem in clinical studies of drug candidates. One of the reasons for development of arrhythmias related to long QT is inhibition of the human ether-a-go-go-related-gene (hERG) potassium channel. Therefore, early prediction of hERG K(+) channel affinity of drug candidates is becoming increasingly important in the drug discovery process. Binary QSAR models with threshold values at IC(50)=1 and of 10 microM, respectively, were generated using two different sets of descriptors. One set comprising 32 P_VSA descriptors and the other one utilizing a set of descriptors identified out of a large set via a feature selection algorithm. For the full dataset, the power for classification of hERG blockers was 82-88%, which meets prior classification models. Considering the fact that 2D descriptors are fast and easy to calculate, these binary QSAR models are versatile tools for use in virtual screening protocols.