title: Tuning HERG out: antitarget QSAR models for drug development.
abstract: Several non-cardiovascular drugs have been withdrawn from the market due to their inhibition of hERG K+ channels that can potentially lead to severe heart arrhythmia and death. As hERG safety testing is a mandatory FDArequired procedure, there is a considerable interest for developing predictive computational tools to identify and filter out potential hERG blockers early in the drug discovery process. In this study, we aimed to generate predictive and well-characterized quantitative structure-activity relationship (QSAR) models for hERG blockage using the largest publicly available dataset of 11,958 compounds from the ChEMBL database. The models have been developed and validated according to OECD guidelines using four types of descriptors and four different machine-learning techniques. The classification accuracies discriminating blockers from non-blockers were as high as 0.83-0.93 on external set. Model interpretation revealed several SAR rules, which can guide structural optimization of some hERG blockers into non-blockers. We have also applied the generated models for screening the World Drug Index (WDI) database and identify putative hERG blockers and non-blockers among currently marketed drugs. The developed models can reliably identify blockers and non-blockers, which could be useful for the scientific community. A freely accessible web server has been developed allowing users to identify putative hERG blockers and non-blockers in chemical libraries of their interest (http://labmol.farmacia.ufg.br/predherg).
authors.forename: Rodolpho C
authors.initials: RC
authors.lastname: Braga
authors.forename: Vinicius M
authors.initials: VM
authors.lastname: Alves
authors.forename: Meryck F B
authors.initials: MF
authors.lastname: Silva
authors.forename: Eugene
authors.initials: E
authors.lastname: Muratov
authors.forename: Denis
authors.initials: D
authors.lastname: Fourches
authors.forename: Alexander
authors.initials: A
authors.lastname: Tropsha
authors.affiliation: LabMol, Faculdade de Farmacia, Universidade Federal de Goias, Rua 240, Qd. 87, Setor Leste Universitario, Goiania, Goias 74605-170, Brazil. carolina@ufg.br.
authors.forename: Carolina H
authors.initials: CH
authors.lastname: Andrade
mesh_terms: D019992:Databases as Topic; D055808:Drug Discovery; D051638:Ether-A-Go-Go Potassium Channels / Q000037:antagonists & inhibitors*; D006801:Humans; D008958:Models, Molecular; D004364:Pharmaceutical Preparations / Q000737:chemistry*; D021281:Quantitative Structure-Activity Relationship
chemical_list: D051638:Ether-A-Go-Go Potassium Channels; D004364:Pharmaceutical Preparations