ADMET evaluation in drug discovery. 12. Development of binary classification models for prediction of hERG potassium channel blockage.

ABSTRACT: Inhibition of the human ether-a-go-go related gene (hERG) potassium channel may result in QT interval prolongation, which causes severe cardiac side effects and is a major problem in clinical studies of drug candidates. The development of in silico tools to filter out potential hERG potassium channel blockers in early stages of the drug discovery process is of considerable interest. Here, a diverse set of 806 compounds with hERG inhibition data was assembled, and the binary hERG classification models using naive Bayesian classification and recursive partitioning (RP) techniques were established and evaluated. The naive Bayesian classifier based on molecular properties and the ECFP_8 fingerprints yielded 84.8% accuracy for the training set using the leave-one-out (LOO) cross-validation proc

SUBMITTER: Wang S 

PROVIDER: S-EPMC3324100 | biostudies-literature | 2012 Apr

REPOSITORIES: biostudies-literature