Predicting GPR40 Agonists with A Deep Learning‐Based Ensemble Model

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) Various GPR40 agonists and non‐agonists for model training and evaluation were collection and used for building and systematically optimizing an ensemble model for predicting GPR40 agonists. The ensemble model was built based on 20 baseline models, which were consisting of different algorithms and molecular representations. And the ensemble model showed greater performance than the performance of any baseline model.