ArticleMolecules (Basel, Switzerland)2020
The Study on the hERG Blocker Prediction Using Chemical Fingerprint Analysis.
Article in Molecules (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Exploring the pharmacological potential of plant-based hand sanitizer composition: characterization, activity evaluation, and in silico assessment.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Mixture of experts for multitask learning in cardiotoxicity assessment.Journal of cheminformatics · 2025Article
- CardioGenAI: a machine learning-based framework for re-engineering drugs for reduced hERG liability.Journal of cheminformatics · 2025Article
- Comparison of new secondgeneration H1 receptor blockers with some molecules; a study involving DFT, molecular docking, ADMET, biological target and activity.BMC chemistry · 2025Article
- AI-driven parametrization of Michaelis-Menten maximal velocity: Advancing in silico new approach methodologies (NAMs).NAM journal · 2025Article
- Identification and therapeutic investigation of biomarker genes underpinning hepatocellular carcinoma: anFrontiers in bioinformatics · 2025Article
- ProTox 3.0: a webserver for the prediction of toxicity of chemicals.Nucleic acids research · 2024Article
- ADis-QSAR: a machine learning model based on biological activity differences of compounds.Journal of computer-aided molecular design · 2023Article
- Unraveling Structural Alerts in Marketed Drugs for Improving Adverse Outcome Pathway Framework of Drug-Induced QT Prolongation.International journal of molecular sciences · 2023Article
- hERG-toxicity prediction using traditional machine learning and advanced deep learning techniques.Current research in toxicology · 2023Article
- Article
- Ligand-based prediction of hERG-mediated cardiotoxicity based on the integration of different machine learning techniques.Frontiers in pharmacology · 2022Article
- Computational Approaches: Drug Discovery and Design in Medicinal Chemistry and Bioinformatics.Molecules (Basel, Switzerland) · 2021Article
- Structure-Based Prediction of hERG-Related Cardiotoxicity: A Benchmark Study.Journal of chemical information and modeling · 2021Article
- Machine learning models for classification tasks related to drug safety.Molecular diversity · 2021Review
- Machine Learning in Arrhythmia and Electrophysiology.Circulation research · 2021Review
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3 authors.
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Abstract
Human ether-a-go-go-related gene (hERG) potassium channel blockage by small molecules may cause severe cardiac side effects. Thus, it is crucial to screen compounds for activity on the hERG channels early in the drug discovery process. In this study, we collected 5299 hERG inhibitors with diverse chemical structures from a number of sources. Based on this dataset, we evaluated different machine learning (ML) and deep learning (DL) algorithms using various integer and binary type fingerprints. A training set of 3991 compounds was used to develop quantitative structure-activity relationship (QSAR) models. The performance of the developed models was evaluated using a test set of 998 compounds. Models were further validated using external set 1 (263 compounds) and external set 2 (47 compounds). Overall, models with integer type fingerprints showed better performance than models with no fingerprints, converted binary type fingerprints or original binary type fingerprints. Comparison of ML and DL algorithms revealed that integer type fingerprints are suitable for ML, whereas binary type fingerprints are suitable for DL. The outcomes of this study indicate that the rational selection of fingerprints is important for hERG blocker prediction.
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