title: ADMET Evaluation in Drug Discovery. 16. Predicting hERG Blockers by Combining Multiple Pharmacophores and Machine Learning Approaches.
abstract: Blockade of human ether-à-go-go related gene (hERG) channel by compounds may lead to drug-induced QT prolongation, arrhythmia, and Torsades de Pointes (TdP), and therefore reliable prediction of hERG liability in the early stages of drug design is quite important to reduce the risk of cardiotoxicity-related attritions in the later development stages. In this study, pharmacophore modeling and machine learning approaches were combined to construct classification models to distinguish hERG active from inactive compounds based on a diverse data set. First, an optimal ensemble of pharmacophore hypotheses that had good capability to differentiate hERG active from inactive compounds was identified by the recursive partitioning (RP) approach. Then, the naive Bayesian classification (NBC) and support vector machine (SVM) approaches were employed to construct classification models by integrating multiple important pharmacophore hypotheses. The integrated classification models showed improved predictive capability over any single pharmacophore hypothesis, suggesting that the broad binding polyspecificity of hERG can only be well characterized by multiple pharmacophores. The best SVM model achieved the prediction accuracies of 84.7% for the training set and 82.1% for the external test set. Notably, the accuracies for the hERG blockers and nonblockers in the test set reached 83.6% and 78.2%, respectively. Analysis of significant pharmacophores helps to understand the multimechanisms of action of hERG blockers. We believe that the combination of pharmacophore modeling and SVM is a powerful strategy to develop reliable theoretical models for the prediction of potential hERG liability.
authors.affiliation: College of Pharmaceutical Sciences, Zhejiang University , Hangzhou, Zhejiang 310058, China.
authors.forename: Shuangquan
authors.initials: S
authors.lastname: Wang
authors.affiliation: College of Pharmaceutical Sciences, Zhejiang University , Hangzhou, Zhejiang 310058, China.
authors.forename: Huiyong
authors.initials: H
authors.lastname: Sun
authors.affiliation: College of Pharmaceutical Sciences, Zhejiang University , Hangzhou, Zhejiang 310058, China.
authors.forename: Hui
authors.initials: H
authors.lastname: Liu
authors.affiliation: College of Pharmaceutical Sciences, Zhejiang University , Hangzhou, Zhejiang 310058, China.
authors.forename: Dan
authors.initials: D
authors.lastname: Li
authors.affiliation: Institute of Functional Nano & Soft Materials (FUNSOM), Soochow University , Suzhou, Jiangsu 215123, China.
authors.forename: Youyong
authors.initials: Y
authors.lastname: Li
authors.affiliation: College of Pharmaceutical Sciences, Zhejiang University , Hangzhou, Zhejiang 310058, China.|State Key Lab of CAD&CG, Zhejiang University , Hangzhou, Zhejiang 310058, P. R. China.
authors.forename: Tingjun
authors.initials: T
authors.lastname: Hou
mesh_terms: D001499:Bayes Theorem; D016000:Cluster Analysis; D055808:Drug Discovery; D000069550:Machine Learning; D008958:Models, Molecular; D026902:Potassium Channel Blockers / Q000737:chemistry; D021281:Quantitative Structure-Activity Relationship; D060388:Support Vector Machine
chemical_list: D026902:Potassium Channel Blockers
keywords: ADMET; SVM; hERG; machine learning; pharmacophore; recursive partitioning; support vector machine