ArticleScientific reports2019
Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- Revisiting ADMET prediction reliability under real-world challenges in the foundation model era.Journal of cheminformatics · 2026Article
- A review on integrated machine learning and deep learning driven artificial intelligence models for pharmacokinetics and toxicokinetics predictions, and their application.Drug metabolism and disposition: the biological fate of chemicals · 2026Review
- Review
- hERG toxicity prediction in early drug discovery using extreme gradient boosting and isometric stratified ensemble mapping.Scientific reports · 2025Article
- Role of artificial intelligence in revolutionizing drug discovery.Fundamental research · 2025Review
- Using the Coefficient of Conformism of a Correlative Prediction in Simulation of Cardiotoxicity.Toxics · 2025Article
- Employing Automated Machine Learning (AutoML) Methods to Facilitate theJournal of chemical information and modeling · 2025Article
- GraphDeep-hERG: Graph Neural Network PharmacoAnalytics for Assessing hERG-Related Cardiotoxicity.Pharmaceutical research · 2025Article
- CardioGenAI: a machine learning-based framework for re-engineering drugs for reduced hERG liability.Journal of cheminformatics · 2025Article
- AttenhERG: a reliable and interpretable graph neural network framework for predicting hERG channel blockers.Journal of cheminformatics · 2024Article
- The Trends and Future Prospective of In Silico Models from the Viewpoint of ADME Evaluation in Drug Discovery.Pharmaceutics · 2023Review
- Derivation of Highly Predictive 3D-QSAR Models for hERG Channel Blockers Based on the Quantum Artificial Neural Network Algorithm.Pharmaceuticals (Basel, Switzerland) · 2023Article
- Revolutionizing Medicinal Chemistry: The Application of Artificial Intelligence (AI) in Early Drug Discovery.Pharmaceuticals (Basel, Switzerland) · 2023Review
- Virtual screening of DrugBank database for hERG blockers using topological Laplacian-assisted AI models.Computers in biology and medicine · 2023Article
- PubChem 2023 update.Nucleic acids research · 2023Article
- Ensemble of structure and ligand-based classification models for hERG liability profiling.Frontiers in pharmacology · 2023Article
- hERG-toxicity prediction using traditional machine learning and advanced deep learning techniques.Current research in toxicology · 2023Article
- Toward Quantitative Models in Safety Assessment: A Case Study to Show Impact of Dose-Response Inference on hERG Inhibition Models.International journal of molecular sciences · 2022Article
- Design and Synthesis of Tranylcypromine-Derived LSD1 Inhibitors with Improved hERG and Microsomal Stability Profiles.ACS medicinal chemistry letters · 2022Article
- Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Assessing the hERG liability in the early stages of drug discovery programs is important. The recent increase of hERG-related information in public databases enabled various successful applications of machine learning techniques to predict hERG inhibition. However, most of these researches constructed the datasets from only one database, limiting the predictability and scope of the models. In this study, a hERG classification model was constructed using the largest dataset for hERG inhibition built by integrating multiple databases. The integrated dataset consisted of more than 291,000 structurally diverse compounds derived from ChEMBL, GOSTAR, PubChem, and hERGCentral. The prediction model was built by support vector machine (SVM) with descriptor selection based on Non-dominated Sorting Genetic Algorithm-II (NSGA-II) to optimize the descriptor set for maximum prediction performance with the minimal number of descriptors. The SVM classification model using 72 selected descriptors and ECFP_4 structural fingerprints recorded kappa statistics of 0.733 and accuracy of 0.984 for the test set, substantially outperforming the prediction performance of the current commercial applications for hERG prediction. Finally, the applicability domain of the prediction model was assessed based on the molecular similarity between the training set and test set compounds.
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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.