ArticleJournal of cheminformatics2024
AttenhERG: a reliable and interpretable graph neural network framework for predicting hERG channel blockers.
Article in Journal of cheminformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- hERG-MFFGNN: An Explainable Deep Learning Model for Predicting Cardiotoxicity Using Multi-feature Fusion and Graph Neural Networks.Interdisciplinary sciences, computational life sciences · 2026Article
- Modeling hERG Channel Liability: From Structural Insight to Highly Accurate Qualitative and Quantitative Models.Journal of chemical information and modeling · 2026Article
- Motif-Level Graph Learning Enables Interpretable Prediction of Drug-Induced QT Prolongation via Cooperative Substructural Determinants.International journal of molecular sciences · 2026Article
- Artificial intelligence for precision oncology from phenotyping and drug discovery to clinical translation.Discover oncology · 2026Review
- Insights and perspectives into the etiology of TKI-induced cardiotoxicity.ASPET discovery · 2026Article
- Determination of bioactive and anti-inflammatory molecules of Thymbra spicata L. from Mardin by GC-MS and LC-Orbitrap HRMS: a DFT, molecular docking, ADMET, biological target and activity study.BMC complementary medicine and therapies · 2025Article
- Advanced machine learning for innovative drug discovery.Journal of cheminformatics · 2025Article
- HERGAI: an artificial intelligence tool for structure-based prediction of hERG inhibitors.Journal of cheminformatics · 2025Article
- Recent advances in AI-based toxicity prediction for drug discovery.Frontiers in chemistry · 2025Review
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Authors and funding
8 authors.
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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 reliability, enhancing its utility in drug discovery and safety assessment. Case studies illustrate the practical application of AttenhERG in optimizing compounds for hERG toxicity, highlighting its potential in rational drug design.Scientific contributionAttenhERG is a breakthrough framework that significantly improves the interpretability and accuracy of predicting hERG channel blockers. By integrating uncertainty estimation, AttenhERG demonstrates superior reliability compared to benchmark models. Two case studies, involving APH1A and NMT1 inhibitors, further emphasize AttenhERG's practical application in compound optimization.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.