ArticleActa pharmacologica Sinica2014
Novel Bayesian classification models for predicting compounds blocking hERG potassium channels.
Article in Acta pharmacologica Sinica, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Design, Synthesis, and Biological Evaluation of Highly Potent Boronic Acid (Boronate Ester)-Bearing Heteroaryldihydropyrimidine Derivatives as HBV Capsid Assembly Modulators.Journal of medicinal chemistry · 2025Article
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- CardioGenAI: a machine learning-based framework for re-engineering drugs for reduced hERG liability.Journal of cheminformatics · 2025Article
- Discovery of carboxyl-containing heteroaryldihydropyrimidine derivatives as novel HBV capsid assembly modulators with significantly improved metabolic stability.RSC medicinal chemistry · 2023Article
- Review of machine learning and deep learning models for toxicity prediction.Experimental biology and medicine (Maywood, N.J.) · 2023Review
- Unraveling Structural Alerts in Marketed Drugs for Improving Adverse Outcome Pathway Framework of Drug-Induced QT Prolongation.International journal of molecular sciences · 2023Article
- Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system.Molecular diversity · 2023Review
- Virtual screening of DrugBank database for hERG blockers using topological Laplacian-assisted AI models.Computers in biology and medicine · 2023Article
- A critical analysis of urea transporter B inhibitors: molecular fingerprints, pharmacophore features for the development of next-generation diuretics.Molecular diversity · 2022Article
- Facing small and biased data dilemma in drug discovery with enhanced federated learning approaches.Science China. Life sciences · 2022Article
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- First structure-activity relationship analysis of SARS-CoV-2 virus main protease (Mpro) inhibitors: an endeavor on COVID-19 drug discovery.Molecular diversity · 2021Article
- Artificial intelligence and machine learning-aided drug discovery in central nervous system diseases: State-of-the-arts and future directions.Medicinal research reviews · 2021Review
- A structure-based computational workflow to predict liability and binding modes of small molecules to hERG.Scientific reports · 2020Article
- The Study on the hERG Blocker Prediction Using Chemical Fingerprint Analysis.Molecules (Basel, Switzerland) · 2020Article
- Support Vector Machine model for hERG inhibitory activities based on the integrated hERG database using descriptor selection by NSGA-II.Scientific reports · 2019Article
- Performance of Machine Learning Algorithms for Qualitative and Quantitative Prediction Drug Blockade of hERG1 channel.Computational toxicology (Amsterdam, Netherlands) · 2018Article
- Experimentally Validated Pharmacoinformatics Approach to Predict hERG Inhibition Potential of New Chemical Entities.Frontiers in pharmacology · 2018Article
- Compilation and physicochemical classification analysis of a diverse hERG inhibition database.Journal of computer-aided molecular design · 2016Article
- Article
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Authors and funding
8 authors.
Funding
Abstract
aimA large number of drug-induced long QT syndromes are ascribed to blockage of hERG potassium channels. The aim of this study was to construct novel computational models to predict compounds blocking hERG channels.
methodsDoddareddy's hERG blockage data containing 2644 compounds were used, which divided into training (2389) and test (255) sets. Laplacian-corrected Bayesian classification models were constructed using Discovery Studio. The models were internally validated with the training set of compounds, and then applied to the test set for validation. Doddareddy's experimentally validated dataset with 60 compounds was used for external test set validation.
resultsA Bayesian classification model considering the effects of four molecular properties (Mw, PPSA, ALogP and pKa_basic) as well as extended-connectivity fingerprints (ECFP_14) exhibited a global accuracy (91%), parameter sensitivity (90%) and specificity (92%) in the test set validation, and a global accuracy (58%), parameter sensitivity (61%) and specificity (57%) in the external test set validation.
conclusionThe novel model is better than those in the literatures for predicting compounds blocking hERG channels, and can be used for large-scale prediction.
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