ArticleJournal of cheminformatics2021
CardioTox net: a robust predictor for hERG channel blockade based on deep learning meta-feature ensembles.
Article in Journal of cheminformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed, 68 citations in OpenAlex.
- AI/ML-based computational models for toxicity prediction.Environmental science and pollution research international · 2026Review
- Motif-Level Graph Learning Enables Interpretable Prediction of Drug-Induced QT Prolongation via Cooperative Substructural Determinants.International journal of molecular sciences · 2026Article
- A Comparative Review of Artificial Intelligence Applications in Small Molecule Versus Peptide Drug Discovery.International journal of molecular sciences · 2026Review
- Improving hERG Cardiotoxicity Prediction via SMILES Transformer, Molecular Fingerprints, and Layer-Wise Self-Adaptive Graph Attention Network.ACS omega · 2026Article
- Review
- Unveiling optimal molecular features for hERG insights with automatic machine learning.Journal of pharmaceutical analysis · 2025Article
- Evaluating Machine Learning Models for Molecular Property Prediction: Performance and Robustness on Out-of-Distribution Data.Journal of chemical information and modeling · 2025Article
- Mixture of experts for multitask learning in cardiotoxicity assessment.Journal of cheminformatics · 2025Article
- Qsarna: An Online Tool for Smart Chemical Space Navigation in Drug Design.Journal of chemical information and modeling · 2025Article
- HERGAI: an artificial intelligence tool for structure-based prediction of hERG inhibitors.Journal of cheminformatics · 2025Article
- druglikeFilter 1.0: An AI powered filter for collectively measuring the drug-likeness of compounds.Journal of pharmaceutical analysis · 2025Article
- hERG toxicity prediction in early drug discovery using extreme gradient boosting and isometric stratified ensemble mapping.Scientific reports · 2025Article
- CardioGenAI: a machine learning-based framework for re-engineering drugs for reduced hERG liability.Journal of cheminformatics · 2025Article
- hERGAT: predicting hERG blockers using graph attention mechanism through atom- and molecule-level interaction analyses.Journal of cheminformatics · 2025Article
- Recent advances in AI-based toxicity prediction for drug discovery.Frontiers in chemistry · 2025Review
- AttenhERG: a reliable and interpretable graph neural network framework for predicting hERG channel blockers.Journal of cheminformatics · 2024Article
- Enhancing hERG Risk Assessment with Interpretable Classificatory and Regression Models.Chemical research in toxicology · 2024Article
- Advancing Drug Safety in Drug Development: Bridging Computational Predictions for Enhanced Toxicity Prediction.Chemical research in toxicology · 2024Review
- Expanding Predictive Capacities in Toxicology: Insights from Hackathon-Enhanced Data and Model Aggregation.Molecules (Basel, Switzerland) · 2024Article
- Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
motivationEther-a-go-go-related gene (hERG) channel blockade by small molecules is a big concern during drug development in the pharmaceutical industry. Blockade of hERG channels may cause prolonged QT intervals that potentially could lead to cardiotoxicity. Various in-silico techniques including deep learning models are widely used to screen out small molecules with potential hERG related toxicity. Most of the published deep learning methods utilize a single type of features which might restrict their performance. Methods based on more than one type of features such as DeepHIT struggle with the aggregation of extracted information. DeepHIT shows better performance when evaluated against one or two accuracy metrics such as negative predictive value (NPV) and sensitivity (SEN) but struggle when evaluated against others such as Matthew correlation coefficient (MCC), accuracy (ACC), positive predictive value (PPV) and specificity (SPE). Therefore, there is a need for a method that can efficiently aggregate information gathered from models based on different chemical representations and boost hERG toxicity prediction over a range of performance metrics.
resultsIn this paper, we propose a deep learning framework based on step-wise training to predict hERG channel blocking activity of small molecules. Our approach utilizes five individual deep learning base models with their respective base features and a separate neural network to combine the outputs of the five base models. By using three external independent test sets with potency activity of IC
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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.