AttenhERG: a reliable and interpretable graph neural network framework for predicting hERG channel blockers.

ABSTRACT: Cardiotoxicity, particularly drug-induced arrhythmias, poses a significant challenge in drug development, highlighting the importance of early-stage prediction of human ether-a-go-go-related gene (hERG) toxicity. hERG encodes the pore-forming subunit of the cardiac potassium channel. Traditional methods are both costly and time-intensive, necessitating the development of computational approaches. In this study, we introduce AttenhERG, a novel graph neural network framework designed to predict hERG channel blockers reliably and interpretably. AttenhERG demonstrates improved performance compared to existing methods with an AUROC of 0.835, showcasing its efficacy in accurately predicting hERG activity across diverse datasets. Additionally, uncertainty evaluation analysis reveals the model's r

SUBMITTER: Yang T 

PROVIDER: S-EPMC11668031 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature