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dc.contributor.advisorGlen, Robert
dc.contributor.authorAdams, Samuel E.
dc.date.accessioned2010-06-01T14:13:14Z
dc.date.available2010-06-01T14:13:14Z
dc.date.issued2010
dc.identifier.otherPhD.33342
dc.identifier.urihttp://www.dspace.cam.ac.uk/handle/1810/225225
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/225225
dc.description.abstractMetaPrint2D, a new software tool implementing a data-mining approach for predicting sites of xenobiotic metabolism has been developed. The algorithm is based on a statistical analysis of the occurrences of atom centred circular fingerprints in both substrates and metabolites. This approach has undergone extensive evaluation and been shown to be of comparable accuracy to current best-in-class tools, but is able to make much faster predictions, for the first time enabling chemists to explore the effects of structural modifications on a compound’s metabolism in a highly responsive and interactive manner.en
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dc.description.abstractMetaPrint2D is able to assign a confidence score to the predictions it generates, based on the availability of relevant data and the degree to which a compound is modelled by the algorithm.en
dc.description.abstract-
dc.description.abstractIn the course of the evaluation of MetaPrint2D a novel metric for assessing the performance of site of metabolism predictions has been introduced. This overcomes the bias introduced by molecule size and the number of sites of metabolism inherent to the most commonly reported metrics used to evaluate site of metabolism predictions.en
dc.description.abstract-
dc.description.abstractThis data mining approach to site of metabolism prediction has been augmented by a set of reaction type definitions to produce MetaPrint2D-React, enabling prediction of the types of transformations a compound is likely to undergo and the metabolites that are formed. This approach has been evaluated against both historical data and metabolic schemes reported in a number of recently published studies. Results suggest that the ability of this method to predict metabolic transformations is highly dependent on the relevance of the training set data to the query compounds.en
dc.description.abstract-
dc.description.abstractMetaPrint2D has been released as an open source software library, and both MetaPrint2D and MetaPrint2D-React are available for chemists to use through the Unilever Centre for Molecular Science Informatics website.en
dc.description.sponsorshipBoehringer-Ingelhiemen
dc.language.isoenen
dc.subjectxenobiotic metabolismen
dc.subjectMetaPrint2Den
dc.subjectmolecular similarityen
dc.subjectcheminformaticsen
dc.subjectchemoinformaticsen
dc.subjectchemical informatics
dc.titleMolecular Similarity and Xenobiotic Metabolismen
dc.typeThesisen
dc.type.qualificationlevelDoctoral
dc.type.qualificationnameDoctor of Philosophy (PhD)
dc.publisher.institutionUniversity of Cambridgeen
dc.publisher.departmentUnilever Centre for Molecular Science Informaticsen
dc.publisher.departmentDepartment of Chemistryen
dc.rights.generalUnder the following condition: * Attribution. You must give the original author credit. * Share Alike. If you alter, transform, or build upon this work, you may distribute the resulting work only under a licence identical to this one.
dc.rights.generalFor any reuse or distribution, you must make clear to others the licence terms of this work. Any of the above conditions can be waived if you get permission from the copyright holder. Nothing in this license impairs or restricts the author's moral rights.
dc.rights.generalTo view the full text of this license, visit http://creativecommons.org/licenses/by-sa/2.0/uk/; or, send a letter to Creative Commons, 171 2nd Street, Suite 300, San Francisco, California, 94105, USA.
dc.rights.generalCopyright © 2010 Samuel Edward Adamsen
dc.rights.generalThis work is licensed under a Creative Commons Attribution-Share Alike 2.0 UK: England & Wales License.
dc.rights.generalThis means that you are free: * to copy, distribute, display, and perform the work * to make derivative works
dc.identifier.doi10.17863/CAM.16274


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