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Prediction of Metabolic Transformations using Cross Venn-ABERS Predictors
Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap. (Spjuth)
AstraZeneca R&D.
Royal Holloway University of London.
Uppsala universitet, Medicinska och farmaceutiska vetenskapsområdet, Farmaceutiska fakulteten, Institutionen för farmaceutisk biovetenskap. Uppsala universitet, Science for Life Laboratory, SciLifeLab.ORCID-id: 0000-0002-8083-2864
2017 (engelsk)Inngår i: Conformal and Probabilistic Prediction with Applications (COPA) 2017 / [ed] Alex Gammerman, Vladimir Vovk, Zhiyuan Luo, Harris Papadopoulos, 2017, Vol. 60, s. 118-131Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
2017. Vol. 60, s. 118-131
Serie
Proceedings of Machine Learning Research, ISSN 1938-7228
HSV kategori
Forskningsprogram
Bioinformatik
Identifikatorer
URN: urn:nbn:se:uu:diva-327948OAI: oai:DiVA.org:uu-327948DiVA, id: diva2:1131218
Konferanse
Conformal and Probabilistic Prediction and Applications, 13-16 June 2017, Stockholm, Sweden
Tilgjengelig fra: 2017-08-13 Laget: 2017-08-13 Sist oppdatert: 2018-01-13

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