ArticleJournal of molecular modeling2012
Predicting the potency of hERG K⁺ channel inhibition by combining 3D-QSAR pharmacophore and 2D-QSAR models.
Article in Journal of molecular modeling, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Derivation of Highly Predictive 3D-QSAR Models for hERG Channel Blockers Based on the Quantum Artificial Neural Network Algorithm.Pharmaceuticals (Basel, Switzerland) · 2023Article
- Structure-Based Prediction of hERG-Related Cardiotoxicity: A Benchmark Study.Journal of chemical information and modeling · 2021Article
- Computational determination of hERG-related cardiotoxicity of drug candidates.BMC bioinformatics · 2019Article
- Capsule Networks Showed Excellent Performance in the Classification of hERG Blockers/Nonblockers.Frontiers in pharmacology · 2019Article
- Pharmacological, Physiochemical, and Drug-Relevant Biological Properties of Short Chain Fatty Acid Hexosamine Analogues Used in Metabolic Glycoengineering.Molecular pharmaceutics · 2018Article
- Experimentally Validated Pharmacoinformatics Approach to Predict hERG Inhibition Potential of New Chemical Entities.Frontiers in pharmacology · 2018Article
- Natural products modulating the hERG channel: heartaches and hope.Natural product reports · 2017Review
- Novel Bayesian classification models for predicting compounds blocking hERG potassium channels.Acta pharmacologica Sinica · 2014Article
- Tuning HERG out: antitarget QSAR models for drug development.Current topics in medicinal chemistry · 2014Article
- Computational methods of studying the binding of toxins from venomous animals to biological ion channels: theory and applications.Physiological reviews · 2013Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Blockade of the hERG K(+) channel has been identified as the most important mechanism of QT interval prolongation and thus inducing cardiac risk. In this work, an ensemble of 3D-QSAR pharmacophore models was constructed to provide insight into the determinants of the interactions between the hERG K(+) channel and channel inhibitors. To predict hERG inhibitory activities, the predicted values from the ensemble of models were averaged, and the results thus obtained showed that the predictive ability of the combined 3D-QSAR pharmacophore model was greater that those of the individual models. Also, using the same training and test sets, a 2D-QSAR model based on a heuristic machine-learning method was developed in order to analyze the physicochemical characters of hERG inhibitors. The models indicated that the inhibitors have certain key inhibitory features in common, including hydrophobicity, aromaticity, and flexibility. A final model was developed by combining the combined 3D-QSAR pharmacophore with the 2D-QSAR model, and this final model outperformed any other individual model, showing the highest predictive ability and the lowest deviation. This model can not only predict hERG inhibitory potency accurately, thus allowing fast cardiac safety evaluation, but it provides an effective tool for avoiding hERG inhibitory liability and thus enhanced cardiac risk in the design and optimization of new chemical entities.
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Registered trials
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