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A k-nearest neighbor classification of hERG K+ channel blockers
Linnaeus Univ, Linnaeus Univ Ctr Biomat Chem, Dept Chem & Biomed Sci, Bioorgan & Biophys Chem Lab, S-39182 Kalmar, Sweden..
eADMET GmbH, Lichtenbergstr 8, D-85748 Munich, Germany..
Linnaeus Univ, Linnaeus Univ Ctr Biomat Chem, Dept Chem & Biomed Sci, Bioorgan & Biophys Chem Lab, S-39182 Kalmar, Sweden..
Uppsala University, Disciplinary Domain of Science and Technology, Chemistry, Department of Chemistry - BMC, Organic Chemistry. Linnaeus Univ, Linnaeus Univ Ctr Biomat Chem, Dept Chem & Biomed Sci, Bioorgan & Biophys Chem Lab, S-39182 Kalmar, Sweden..
2016 (English)In: Journal of Computer-Aided Molecular Design, ISSN 0920-654X, E-ISSN 1573-4951, Vol. 30, no 3, 229-236 p.Article in journal (Refereed) Published
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Text
Abstract [en]

A series of 172 molecular structures that block the hERG K+ channel were used to develop a classification model where, initially, eight types of PaDEL fingerprints were used for k-nearest neighbor model development. A consensus model constructed using Extended-CDK, PubChem and Substructure count fingerprint-based models was found to be a robust predictor of hERG activity. This consensus model demonstrated sensitivity and specificity values of 0.78 and 0.61 for the internal dataset compounds and 0.63 and 0.54 for the external (PubChem) dataset compounds, respectively. This model has identified the highest number of true positives (i.e. 140) from the PubChem dataset so far, as compared to other published models, and can potentially serve as a basis for the prediction of hERG active compounds. Validating this model against FDA-withdrawn substances indicated that it may even be useful for differentiating between mechanisms underlying QT prolongation.

Place, publisher, year, edition, pages
2016. Vol. 30, no 3, 229-236 p.
Keyword [en]
Classification model, hERG blockers, Ikr, KCNH2, k-nearest neighbor (k-NN), Toxicity
National Category
Chemical Sciences Computer and Information Science
Identifiers
URN: urn:nbn:se:uu:diva-294332DOI: 10.1007/s10822-016-9898-zISI: 000373117200004PubMedID: 26860111OAI: oai:DiVA.org:uu-294332DiVA: diva2:929481
Funder
EU, FP7, Seventh Framework Programme, 238701
Available from: 2016-05-18 Created: 2016-05-18 Last updated: 2017-11-30Bibliographically approved

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