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Willett, P. (2011) Similarity-based data mining in files of two-dimensional chemical structures using fingerprint measures of molecular resemblance. Wiley Interdisciplinary Reviews - Data Mining and Knowledge Discovery, 1 (3). 241 - 251. ISSN 1942-4787
Abstract
This paper reviews the use of measures of intermolecular similarity for processing databases of chemical structures, which play an important role in the discovery of new drugs by the pharmaceutical industry. The similarity measures considered here are based on the use of a fingerprint representation of molecular structure, where a fingerprint is a vector encoding the presence of fragment substructures in a molecule and where the similarity between pairs of such fingerprints is computed using an association coefficient such as the Tanimoto coefficient. The Similar Property Principle provides the basic rationale for the use of similarity methods in three important chemoinformatics applications—similarity searching, database clustering, and molecular diversity analysis. Similarity searching enables the identification of those molecules in a database that are most similar to a user-defined, biologically active query molecule, with data fusion providing an effective way of combining the results of multiple similarity searches. Cluster analysis, typically using the Jarvis–Patrick, Ward, or divisive k-means clustering methods, enables the cost-effective selection of molecules for biological testing, for property prediction and for investigating database overlap. Molecular diversity analysis, typically using cluster-based, dissimilarity-based, or optimization-based approaches, enables the identification of structurally diverse sets of molecules, so as to ensure that the full chemical space spanned by a database is tested in the search for novel bioactive molecules. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 241–251 DOI: 10.1002/widm.26
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2011 Wiley-Blackwell. This is an author produced version of a paper subsequently published in Wiley Interdisciplinary Reviews - Data Mining and Knowledge Discovery. Uploaded in accordance with the publisher's self-archiving policy.
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Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > Information School (Sheffield) |
Depositing User: | Miss Anthea Tucker |
Date Deposited: | 20 Nov 2013 15:49 |
Last Modified: | 20 Nov 2013 15:49 |
Published Version: | http://dx.doi.org/10.1002/widm.26 |
Status: | Published |
Publisher: | Wiley-Blackwell |
Identification Number: | 10.1002/widm.26 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:76256 |
Available Versions of this Item
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Similarity-based data mining in files of two-dimensional chemical structures using fingerprint measures of molecular resemblance. (deposited 03 Jul 2012 10:19)
- Similarity-based data mining in files of two-dimensional chemical structures using fingerprint measures of molecular resemblance. (deposited 20 Nov 2013 15:49) [Currently Displayed]