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Prediction of Metabolic Transformations using Cross Venn-ABERS Predictors
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (Spjuth)
AstraZeneca R&D.
Royal Holloway University of London.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. Uppsala University, Science for Life Laboratory, SciLifeLab.ORCID iD: 0000-0002-8083-2864
2017 (English)In: Conformal and Probabilistic Prediction with Applications (COPA) 2017 / [ed] Alex Gammerman, Vladimir Vovk, Zhiyuan Luo, Harris Papadopoulos, 2017, Vol. 60, p. 118-131Conference paper, Published paper (Refereed)
Abstract [en]

Prediction of drug metabolism is an important topic in the drug discovery process, and we here present a study using probabilistic predictions applying Cross Venn-ABERS Predictors (CVAPs) on data for site-of-metabolism. We used a dataset of 73599 biotransformations, applied SMIRKS to define biotransformations of interest and constructed five datasets where chemical structures were represented using signatures descriptors. The results show that CVAP produces well-calibrated predictions for all datasets with good predictive capability, making CVAP an interesting method for further exploration in drug discovery applications.

Place, publisher, year, edition, pages
2017. Vol. 60, p. 118-131
Series
Proceedings of Machine Learning Research, ISSN 1938-7228
National Category
Bioinformatics (Computational Biology)
Research subject
Bioinformatics
Identifiers
URN: urn:nbn:se:uu:diva-327948OAI: oai:DiVA.org:uu-327948DiVA, id: diva2:1131218
Conference
Conformal and Probabilistic Prediction and Applications, 13-16 June 2017, Stockholm, Sweden
Available from: 2017-08-13 Created: 2017-08-13 Last updated: 2018-01-13

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