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Prediction of hERG K+ channel blockage using deep neural networks

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/minf.201500040openalex-snapshot

Pred‐hERG: A Novel web‐Accessible Computational Tool for Predicting Cardiac Toxicity

inferredcites →https://doi.org/10.1002/minf.201700074openalex-snapshot

Modeling of the hERG K+ Channel Blockage Using Online Chemical Database and Modeling Environment (OCHEM)

inferredcites →https://doi.org/10.1007/s10822-016-9898-zopenalex-snapshot

A k-nearest neighbor classification of hERG K+ channel blockers

inferredcites →https://doi.org/10.1007/s10822-016-9986-0openalex-snapshot

Compilation and physicochemical classification analysis of a diverse hERG inhibition database

inferredcites →https://doi.org/10.1021/acs.jcim.5b00190openalex-snapshot

IChemPIC: A Random Forest Classifier of Biological and Crystallographic Protein–Protein Interfaces

inferredcites →https://doi.org/10.1021/acs.jcim.5b00616openalex-snapshot

General Purpose 2D and 3D Similarity Approach to Identify hERG Blockers

inferredcites →https://doi.org/10.1021/acs.jcim.5b00642openalex-snapshot

ADME Properties Evaluation in Drug Discovery: Prediction of Caco-2 Cell Permeability Using a Combination of NSGA-II and Boosting

inferredcites →https://doi.org/10.1021/acs.jcim.6b00426openalex-snapshot

Creating the New from the Old: Combinatorial Libraries Generation with Machine-Learning-Based Compound Structure Optimization

inferredcites →https://doi.org/10.1021/acs.jcim.6b00625openalex-snapshot

In Silico Prediction of Physicochemical Properties of Environmental Chemicals Using Molecular Fingerprints and Machine Learning

inferredcites →https://doi.org/10.1021/acs.jcim.6b00753openalex-snapshot

Comparison of the Predictive Performance and Interpretability of Random Forest and Linear Models on Benchmark Data Sets

inferredcites →https://doi.org/10.1021/acs.jcim.7b00087openalex-snapshot

Demystifying Multitask Deep Neural Networks for Quantitative Structure–Activity Relationships

inferredcites →https://doi.org/10.1021/acs.jcim.7b00146openalex-snapshot

Is Multitask Deep Learning Practical for Pharma?

inferredcites →https://doi.org/10.1021/acs.jcim.7b00656openalex-snapshot

Multiclassification Prediction of Enzymatic Reactions for Oxidoreductases and Hydrolases Using Reaction Fingerprints and Machine Learning Methods

inferredcites →https://doi.org/10.1021/acs.jcim.8b00150openalex-snapshot

The Catch-22 of Predicting hERG Blockade Using Publicly Accessible Bioactivity Data

inferredcites →https://doi.org/10.1021/acs.jcim.8b00769openalex-snapshot

Deep Learning-Based Prediction of Drug-Induced Cardiotoxicity

inferredcites →https://doi.org/10.1021/acs.molpharmaceut.6b00471openalex-snapshot

ADMET Evaluation in Drug Discovery. 16. Predicting hERG Blockers by Combining Multiple Pharmacophores and Machine Learning Approaches

inferredcites →https://doi.org/10.1021/acs.molpharmaceut.7b00578openalex-snapshot

Comparison of Deep Learning With Multiple Machine Learning Methods and Metrics Using Diverse Drug Discovery Data Sets

inferredcites →https://doi.org/10.1021/ci00028a014openalex-snapshot

Electrotopological State Indices for Atom Types: A Novel Combination of Electronic, Topological, and Valence State Information

inferredcites →https://doi.org/10.1021/ci034160gopenalex-snapshot

Random Forest: A Classification and Regression Tool for Compound Classification and QSAR Modeling

inferredcites →https://doi.org/10.1021/ci100081jopenalex-snapshot

In Silico Binary Classification QSAR Models Based on 4D-Fingerprints and MOE Descriptors for Prediction of hERG Blockage

inferredcites →https://doi.org/10.1021/ci200615hopenalex-snapshot

Comparison of Random Forest and Pipeline Pilot Naïve Bayes in Prospective QSAR Predictions

inferredcites →https://doi.org/10.1021/ci300060bopenalex-snapshot

Predictive Toxicology Modeling: Protocols for Exploring hERG Classification and Tetrahymena pyriformis End Point Predictions

inferredcites →https://doi.org/10.1021/ci500747nopenalex-snapshot

Deep Neural Nets as a Method for Quantitative Structure–Activity Relationships

inferredcites →https://doi.org/10.1021/ci900203nopenalex-snapshot

Application of Random Forest Approach to QSAR Prediction of Aquatic Toxicity

inferredcites →https://doi.org/10.1021/mp300023xopenalex-snapshot

ADMET Evaluation in Drug Discovery. 12. Development of Binary Classification Models for Prediction of hERG Potassium Channel Blockage

inferredcites →https://doi.org/10.1021/mp700124eopenalex-snapshot

hERG Classification Model Based on a Combination of Support Vector Machine Method and GRIND Descriptors

inferredcites →https://doi.org/10.1021/tx500389qopenalex-snapshot

Absorption, Distribution, Metabolism, Excretion, and Toxicity Evaluation in Drug Discovery. 14. Prediction of Human Pregnane X Receptor Activators by Using Naive Bayesian Classification Technique

inferredcites →https://doi.org/10.1038/nature14539openalex-snapshot

Deep learning

inferredcites →https://doi.org/10.1093/bioinformatics/bty374openalex-snapshot

Development and evaluation of a deep learning model for protein–ligand binding affinity prediction

inferredcites →https://doi.org/10.1093/nar/gkv951openalex-snapshot

PubChem Substance and Compound databases

inferredcites →https://doi.org/10.1109/tkde.2008.239openalex-snapshot

Learning from Imbalanced Data

inferredcites →https://doi.org/10.1186/s13321-016-0117-7openalex-snapshot

ADMET evaluation in drug discovery: 15. Accurate prediction of rat oral acute toxicity using relevance vector machine and consensus modeling

inferredcites →https://doi.org/10.1186/s13321-017-0230-2openalex-snapshot

Efficiency of different measures for defining the applicability domain of classification models

inferredcites →https://doi.org/10.2174/1568026614666140506124442openalex-snapshot

Tuning hERG Out: Antitarget QSAR Models for Drug Development

inferredcites →https://doi.org/10.4155/fmc.15.18openalex-snapshot

In Silico Prediction of hERG Inhibition

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1002/slct.202201221openalex-snapshot

A Review of Computational Methods in Predicting hERG Channel Blockers

inferred← citeshttps://doi.org/10.1002/wcms.1475openalex-snapshot

In silico toxicology: From structure–activity relationships towards deep learning and adverse outcome pathways

inferred← citeshttps://doi.org/10.1002/wcms.1568openalex-snapshot

Machine learning in drug design: Use of artificial intelligence to explore the chemical structure–biological activity relationship

inferred← citeshttps://doi.org/10.1007/978-1-0716-1787-8_16openalex-snapshot

Artificial Intelligence, Machine Learning, and Deep Learning in Real-Life Drug Design Cases

inferred← citeshttps://doi.org/10.1007/978-1-0716-1787-8_22openalex-snapshot

Artificial Intelligence in Drug Safety and Metabolism

inferred← citeshttps://doi.org/10.1007/978-981-95-2525-6_48openalex-snapshot

Drug Toxicity Prediction

inferred← citeshttps://doi.org/10.1007/s11095-025-03848-wopenalex-snapshot

GraphDeep-hERG: Graph Neural Network PharmacoAnalytics for Assessing hERG-Related Cardiotoxicity

inferred← citeshttps://doi.org/10.1007/s12010-024-05110-2openalex-snapshot

Decoding Drug Discovery: Exploring A-to-Z In Silico Methods for Beginners

inferred← citeshttps://doi.org/10.1007/s12257-020-0049-yopenalex-snapshot

Artificial Intelligence in Drug Discovery: A Comprehensive Review of Data-driven and Machine Learning Approaches

inferred← citeshttps://doi.org/10.1016/b978-0-12-824010-6.00063-0openalex-snapshot

Computational precision therapeutics and drug repositioning

inferred← citeshttps://doi.org/10.1016/j.aichem.2023.100038openalex-snapshot

AI's role in pharmaceuticals: Assisting drug design from protein interactions to drug development

inferred← citeshttps://doi.org/10.1016/j.apsb.2026.05.028openalex-snapshot

AI and machine learning for predicting in vitro/preclinical drug toxicities and adverse events in clinical patients

inferred← citeshttps://doi.org/10.1016/j.comtox.2022.100228openalex-snapshot

Towards quantifying the uncertainty in in silico predictions using Bayesian learning

inferred← citeshttps://doi.org/10.1016/j.crtox.2023.100121openalex-snapshot

hERG-toxicity prediction using traditional machine learning and advanced deep learning techniques

inferred← citeshttps://doi.org/10.1016/j.vascn.2023.107293openalex-snapshot

hERG agonists pose challenges to web-based machine learning methods for prediction of drug-hERG channel interaction

inferred← citeshttps://doi.org/10.1016/j.yjmcc.2023.05.002openalex-snapshot

Prediction of Kv11.1 potassium channel PAS-domain variants trafficking via machine learning

inferred← citeshttps://doi.org/10.1021/acs.chemrestox.0c00316openalex-snapshot

Machine Learning in Predictive Toxicology: Recent Applications and Future Directions for Classification Models

inferred← citeshttps://doi.org/10.1021/acs.jcim.1c00744openalex-snapshot

Structure-Based Prediction of hERG-Related Cardiotoxicity: A Benchmark Study

inferred← citeshttps://doi.org/10.1021/acs.jcim.2c00256openalex-snapshot

HergSPred: Accurate Classification of hERG Blockers/Nonblockers with Machine-Learning Models

inferred← citeshttps://doi.org/10.1021/acs.jcim.3c01301openalex-snapshot

Benchmarking of Small Molecule Feature Representations for hERG, Nav1.5, and Cav1.2 Cardiotoxicity Prediction

inferred← citeshttps://doi.org/10.1021/acs.jcim.4c01102openalex-snapshot

Semisupervised Learning to Boost hERG, Nav1.5, and Cav1.2 Cardiac Ion Channel Toxicity Prediction by Mining a Large Unlabeled Small Molecule Data Set

inferred← citeshttps://doi.org/10.1101/2020.09.28.317461openalex-snapshot

A deep learning algorithm to translate and classify cardiac electrophysiology: From induced pluripotent stem cell-derived cardiomyocytes to adult cardiac cells

inferred← citeshttps://doi.org/10.1101/2024.05.25.595894openalex-snapshot

Semi-Supervised Learning to Boost Cardiotoxicity Prediction by Mining a Large Unlabeled Small Molecule Dataset

inferred← citeshttps://doi.org/10.1101/2025.02.17.638731openalex-snapshot

hERG-LTN: A New Paradigm in hERG Cardiotoxicity Assessment Using Neuro-Symbolic and Generative AI Embedding (MegaMolBART, Llama3.2, Gemini, DeepSeek) Approach

inferred← citeshttps://doi.org/10.1109/embc58623.2025.11253500openalex-snapshot

hERG-LTN: A New Paradigm for hERG Cardiotoxicity Prediction Using Neuro-Symbolic and Generative AI

inferred← citeshttps://doi.org/10.1111/cbdd.13742openalex-snapshot

A natural language processing approach based on embedding deep learning from heterogeneous compounds for quantitative structure–activity relationship modeling

inferred← citeshttps://doi.org/10.1111/cbdd.13894openalex-snapshot

In silico prediction of drug‐induced ototoxicity using machine learning and deep learning methods

inferred← citeshttps://doi.org/10.1177/1535370221993422openalex-snapshot

Trends in application of advancing computational approaches in GPCR ligand discovery

inferred← citeshttps://doi.org/10.1177/15353702231209421openalex-snapshot

Review of machine learning and deep learning models for toxicity prediction

inferred← citeshttps://doi.org/10.1186/s13321-025-00957-xopenalex-snapshot

hERGAT: predicting hERG blockers using graph attention mechanism through atom- and molecule-level interaction analyses

inferred← citeshttps://doi.org/10.1186/s13321-025-00976-8openalex-snapshot

CardioGenAI: a machine learning-based framework for re-engineering drugs for reduced hERG liability

inferred← citeshttps://doi.org/10.21203/rs.3.rs-1630646/v1openalex-snapshot

ToxTree: Descriptor-based machine learning models to predict hERG and Nav1.5 cardiotoxicity

inferred← citeshttps://doi.org/10.2174/0127724328441651260211220550openalex-snapshot

Data-Driven Toxicity Prediction: Advances in Machine Learning, Deep Learning, and Predictive Tools - A Systematic Review

inferred← citeshttps://doi.org/10.3389/fchem.2025.1632046openalex-snapshot

Recent advances in AI-based toxicity prediction for drug discovery

inferred← citeshttps://doi.org/10.3390/chemistry4030045openalex-snapshot

hERG Blockade Prediction by Combining Site Identification by Ligand Competitive Saturation and Physicochemical Properties

inferred← citeshttps://doi.org/10.3390/ijms23137389openalex-snapshot

Mutation-Specific Differences in Kv7.1 (KCNQ1) and Kv11.1 (KCNH2) Channel Dysfunction and Long QT Syndrome Phenotypes

inferred← citeshttps://doi.org/10.3390/molecules25112615openalex-snapshot

The Study on the hERG Blocker Prediction Using Chemical Fingerprint Analysis

inferred← citeshttps://doi.org/10.7554/elife.68335openalex-snapshot

A deep learning algorithm to translate and classify cardiac electrophysiology

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