Search

Recent studies have identified G protein‐coupled receptor 40 (GPR40) as a promising target for treating type 2 diabetes mellitus, and GPR40 agonists have several superior effects over other hypoglycemic drugs, including cardiovascular protection and suppression of glucagon levels. In this study, we constructed an up‐to‐date GPR40 ligand dataset for training models and performed a systematic optimization of the ensemble model, resulting in a powerful ensemble model (ROC AUC: 0.9496) for distinguishing GPR40 agonists and non‐agonists. The ensemble model is divided into three layers, and the optimization process is carried out in each layer. We believe that these results will prove helpful for both the development of GPR40 agonists and ensemble models. All the data and models are available on GitHub. (https://github.com/Jiamin‐Yang/ensemble_model) [ABSTRACT FROM AUTHOR]

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 FROM AUTHOR]