ArticleCurrent research in toxicology2023
hERG-toxicity prediction using traditional machine learning and advanced deep learning techniques.
Article in Current research in toxicology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- AI/ML-based computational models for toxicity prediction.Environmental science and pollution research international · 2026Review
- Integrated assessment of developmental toxicity of antibiotic pollutants: Machine learning prediction, zebrafish validation, and network toxicology.Molecular diversity · 2026Article
- LEP-AD: language embedding of proteins and attention to drugs predicts drug-target interactions.Journal of cheminformatics · 2026Article
- Progress and future outlook towards a safe and sustainable production and use of chemicals.Communications chemistry · 2025Review
- HERGAI: an artificial intelligence tool for structure-based prediction of hERG inhibitors.Journal of cheminformatics · 2025Article
- hERG toxicity prediction in early drug discovery using extreme gradient boosting and isometric stratified ensemble mapping.Scientific reports · 2025Article
- GraphDeep-hERG: Graph Neural Network PharmacoAnalytics for Assessing hERG-Related Cardiotoxicity.Pharmaceutical research · 2025Article
- hERGAT: predicting hERG blockers using graph attention mechanism through atom- and molecule-level interaction analyses.Journal of cheminformatics · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The rise of artificial intelligence (AI) based algorithms has gained a lot of interest in the pharmaceutical development field. Our study demonstrates utilization of traditional machine learning techniques such as random forest (RF), support-vector machine (SVM), extreme gradient boosting (XGBoost), deep neural network (DNN) as well as advanced deep learning techniques like gated recurrent unit-based DNN (GRU-DNN) and graph neural network (GNN), towards predicting human ether-á-go-go related gene (hERG) derived toxicity. Using the largest hERG dataset derived to date, we have utilized 203,853 and 87,366 compounds for training and testing the models, respectively. The results show that GNN, SVM, XGBoost, DNN, RF, and GRU-DNN all performed well, with validation set AUC ROC scores equals 0.96, 0.95, 0.95, 0.94, 0.94 and 0.94, respectively. The GNN was found to be the top performing model based on predictive power and generalizability. The GNN technique is free of any feature engineering steps while having a minimal human intervention. The GNN approach may serve as a basis for comprehensive automation in predictive toxicology. We believe that the models presented here may serve as a promising tool, both for academic institutes as well as pharmaceutical industries, in predicting hERG-liability in new molecular structures.
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Registered trials
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.