ArticleJournal of cheminformatics2019
Avoiding hERG-liability in drug design via synergetic combinations of different (Q)SAR methodologies and data sources: a case study in an industrial setting.
Article in Journal of cheminformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Approaching Pharmacological Space: Events and Components.Methods in molecular biology (Clifton, N.J.) · 2025Article
- The ChEMBL Database in 2023: a drug discovery platform spanning multiple bioactivity data types and time periods.Nucleic acids research · 2024Article
- Derivation of Highly Predictive 3D-QSAR Models for hERG Channel Blockers Based on the Quantum Artificial Neural Network Algorithm.Pharmaceuticals (Basel, Switzerland) · 2023Article
- ProfhEX: AI-based platform for small molecules liability profiling.Journal of cheminformatics · 2023Article
- In silico analysis of noscapine compounds as anti-tumor agents targeting the tubulin receptor.Journal of Taibah University Medical Sciences · 2023Article
- In silico activity and ADMET profiling of phytochemicals from Ethiopian indigenous aloes using pharmacophore models.Scientific reports · 2022Article
- Angiotensin converting enzyme inhibitors from medicinal plants: a molecular docking and dynamic simulation approach.In silico pharmacology · 2022Article
- Use of Lhasa Limited Products for the In Silico Prediction of Drug Toxicity.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Exploring the inhibitory potentials ofIn silico pharmacology · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this paper, we explore the impact of combining different in silico prediction approaches and data sources on the predictive performance of the resulting system. We use inhibition of the hERG ion channel target as the endpoint for this study as it constitutes a key safety concern in drug development and a potential cause of attrition. We will show that combining data sources can improve the relevance of the training set in regard of the target chemical space, leading to improved performance. Similarly we will demonstrate that combining multiple statistical models together, and with expert systems, can lead to positive synergistic effects when taking into account the confidence in the predictions of the merged systems. The best combinations analyzed display a good hERG predictivity. Finally, this work demonstrates the suitability of the SOHN methodology for building models in the context of receptor based endpoints like hERG inhibition when using the appropriate pharmacophoric descriptors.
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Registered trials
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