ADMET Evaluation in Drug Discovery.12.Development of Binary Classification Models for Prediction of hERG Potassium Channel Blockage

摘要

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 bERG potassium channel blockers in early stages of the drug discovery process is of considerable interest.Here,a diverse set of 806 compounds with bERG inhibition data was assembled,and the binary hERG dassification 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 procedure and 85％ accuracy for the test set of 120 molecules For the two additional test sets,the modal achieved 89.4％ accuracy for the WOMBAT-PK test set,and 86.1％ accuracy for the PubChem test set.The naive Bayesian classifiers gave better predictions than the RP classifiers.Moreover,the Bayesian classifier,employing molecular fingerprints,highlights the important structural fragments favorable or unfavorable for hERG potassium channel blockage,which offers extra valuable information for the design of compounds avoiding undesirable hERG activity.