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A confidence predictor for logD using conformal regression and a support-vector machine
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (Spjuth)ORCID iD: 0000-0002-0122-6680
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (Spjuth)ORCID iD: 0000-0001-6709-7116
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab. (Spjuth)ORCID iD: 0000-0001-6740-9212
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (Spjuth)
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2018 (English)In: Journal of Cheminformatics, ISSN 1758-2946, E-ISSN 1758-2946, Vol. 10, no 1, article id 17Article in journal (Refereed) Published
Abstract [en]

Lipophilicity is a major determinant of ADMET properties and overall suitability of drug candidates. We have developed large-scale models to predict water-octanol distribution coefficient (logD) for chemical compounds, aiding drug discovery projects. Using ACD/logD data for 1.6 million compounds from the ChEMBL database, models are created and evaluated by a support-vector machine with a linear kernel using conformal prediction methodology, outputting prediction intervals at a specified confidence level. The resulting model shows a predictive ability of [Formula: see text] and with the best performing nonconformity measure having median prediction interval of [Formula: see text] log units at 80% confidence and [Formula: see text] log units at 90% confidence. The model is available as an online service via an OpenAPI interface, a web page with a molecular editor, and we also publish predictive values at 90% confidence level for 91 M PubChem structures in RDF format for download and as an URI resolver service.

Place, publisher, year, edition, pages
2018. Vol. 10, no 1, article id 17
Keywords [en]
Conformal prediction, LogD, Machine learning, QSAR, RDF, Support-vector machine
National Category
Bioinformatics (Computational Biology)
Research subject
Bioinformatics
Identifiers
URN: urn:nbn:se:uu:diva-347779DOI: 10.1186/s13321-018-0271-1ISI: 000429065900001PubMedID: 29616425OAI: oai:DiVA.org:uu-347779DiVA, id: diva2:1195839
Funder
EU, Horizon 2020, 731075Available from: 2018-04-06 Created: 2018-04-06 Last updated: 2018-06-13Bibliographically approved

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Lapins, MarisLampa, SamuelBerg, ArvidSchaal, WesleyAlvarsson, JonathanSpjuth, Ola

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Lapins, MarisArvidsson, StaffanLampa, SamuelBerg, ArvidSchaal, WesleyAlvarsson, JonathanSpjuth, Ola
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Department of Pharmaceutical BiosciencesScience for Life Laboratory, SciLifeLab
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Journal of Cheminformatics
Bioinformatics (Computational Biology)

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