Show simple item record

dc.contributor.authorTyzack, Jonathan Den
dc.contributor.authorMussa, Hamse Yen
dc.contributor.authorWilliamson, Marken
dc.contributor.authorKirchmair, Johannesen
dc.contributor.authorGlen, Roberten
dc.date.accessioned2015-06-01T14:07:10Z
dc.date.available2015-06-01T14:07:10Z
dc.date.issued2014-05-27en
dc.identifier.citationJournal of Cheminformatics 2014, 6:29. doi: 10.1186/1758-2946-6-29en
dc.identifier.issn1758-2946
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/248132
dc.description.abstractBackground: The prediction of sites and products of metabolism in xenobiotic compounds is key to the development of new chemical entities, where screening potential metabolites for toxicity or unwanted side-effects is of crucial importance. In this work 2D topological fingerprints are used to encode atomic sites and three probabilistic machine learning methods are applied: Parzen-Rosenblatt Window (PRW), Naive Bayesian (NB) and a novel approach called RASCAL (Random Attribute Subsampling Classification ALgorithm). These are implemented by randomly subsampling descriptor space to alleviate the problem often suffered by data mining methods of having to exactly match fingerprints, and in the case of PRW by measuring a distance between feature vectors rather than exact matching. The classifiers have been implemented in CUDA/C++ to exploit the parallel architecture of graphical processing units (GPUs) and is freely available in a public repository. Results: It is shown that for PRW a SoM (Site of Metabolism) is identified in the top two predictions for 85%, 91% and 88% of the CYP 3A4, 2D6 and 2C9 data sets respectively, with RASCAL giving similar performance of 83%, 91% and 88%, respectively. These results put PRW and RASCAL performance ahead of NB which gave a much lower classification performance of 51%, 73% and 74%, respectively. Conclusions: 2D topological fingerprints calculated to a bond depth of 4-6 contain sufficient information to allow the identification of SoMs using classifiers based on relatively small data sets. Thus, the machine learning methods outlined in this paper are conceptually simpler and more efficient than other methods tested and the use of simple topological descriptors derived from 2D structure give results competitive with other approaches using more expensive quantum chemical descriptors. The descriptor space subsampling approach and ensemble methodology allow the methods to be applied to molecules more distant from the training data where data mining would be more likely to fail due to the lack of common fingerprints. The RASCAL algorithm is shown to give equivalent classification performance to PRW but at lower computational expense allowing it to be applied more efficiently in the ensemble scheme.
dc.description.sponsorshipThe authors would like to thank Unilever for funding. We thank Dr. Guus Duchateau, Leo van Buren and Prof. Werner Klaffke for useful discussions in the development of this work.
dc.languageEnglishen
dc.language.isoenen
dc.publisherChemistry Central
dc.rightsAttribution 2.0 UK: England & Wales*
dc.rights.urihttp://creativecommons.org/licenses/by/2.0/uk/*
dc.subjectCytochrome P450en
dc.subjectMetabolismen
dc.subjectProbabilisticen
dc.subjectClassificationen
dc.subjectGPUen
dc.subjectCUDAen
dc.subject2Den
dc.titleCytochrome P450 site of metabolism prediction from 2D topological fingerprints using GPU accelerated probabilistic classifiersen
dc.typeArticle
dc.description.versionThis is the final version. It was first published by Chemistry Central at http://www.jcheminf.com/content/6/1/29.en
prism.number29en
prism.publicationDate2014en
prism.publicationNameJournal of Cheminformaticsen
prism.volume6en
dcterms.dateAccepted2014-05-12en
rioxxterms.versionofrecord10.1186/1758-2946-6-29en
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2014-05-27en
dc.contributor.orcidWilliamson, Mark [0000-0002-5295-7811]
dc.contributor.orcidGlen, Robert [0000-0003-1759-2914]
dc.identifier.eissn1758-2946
rioxxterms.typeJournal Article/Reviewen


Files in this item

Thumbnail
Thumbnail

This item appears in the following Collection(s)

Show simple item record

Attribution 2.0 UK: England & Wales
Except where otherwise noted, this item's licence is described as Attribution 2.0 UK: England & Wales