Molecular Similarity and Xenobiotic Metabolism

MetaPrint2D, 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.

Name dc:type.qualificationname

Doctor of Philosophy (PhD)

Level dc:type.qualificationlevel

Doctoral

Grantor dc:publisher.institution

University of Cambridge

Year dc:date.issued

2010

Author dc:creator

• Adams, Samuel E.

• xenobiotic metabolism
• MetaPrint2D
• molecular similarity
• cheminformatics
• chemoinformatics
• chemical informatics

Statement dc:rights

• Under 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.
• For 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.
• To 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.
• This work is licensed under a Creative Commons Attribution-Share Alike 2.0 UK: England & Wales License.
• This means that you are free: * to copy, distribute, display, and perform the work * to make derivative works
• Copyright © 2010 Samuel Edward Adams

Licence

• https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/e936a59b-b6a8-4ebb-8bbd-3e3a3138c81a/download
• https://www.rioxx.net/licenses/all-rights-reserved/

Language dc:language

en

DOI dc:identifier.doi

https://doi.org/10.17863/CAM.16274

OAI identifier oai:identifier

oai:www.repository.cam.ac.uk:1810/225225