A Review of Computational Methods in Predicting hERG Channel Blockers. Issue 31 (18th August 2022)

• Record Type: Journal Article
• Authors: Shan, Mengyi; Jiang, Chen; Qin, Lu‐Ping; Cheng, Gang
• Is Part Of: ChemistrySelect. Volume 7:Issue 31(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2022-08-18
• Subjects: hERG -- Inhibitors -- Ligand-based -- Machine Learning -- Virtual Screening; Chemistry -- Periodicals; 540.5
• DOI: 10.1002/slct.202201221
• Languages: English
• ISSNs: 2365-6549

Abstract

Abstract: Inhibition of the human ether‐à‐go‐go‐related gene (hERG) ion channel may lead to prolonged QT interval and even fatal ventricular arrhythmia. Therefore, evaluating the hERG liability has become a mandatory requirement for drug development and drug safety process. However, standard experimental assays are complex, expensive and time‐consuming. Various in silico tools have been developed to identify potential hERG blockers and reduce in advance the risk of cardiotoxicity‐related attritions during the early drug discovery phase. In this review, we first outlined the structure and gating properties of the hERG channel briefly. Then analyzed the advantages of computational technology, and comprehensively summarized the in silico methods of hERG‐related cardiotoxicity, including ligand‐based and structure‐based methods. Additionally, the current predictive models in prediction of hERG blockage were emphatically reviewed. Finally, issues involved in the current computational methods, as well as the future direction for developing reliable models of hERG channel blockers, are discussed. Abstract : Computational methods have been applied to predict the potential hERG blockage of novel drug candidates, we provided new insights for developing reliable models and emphatically reviewed the current predictive models about computational investigations of hERG channel blockers.