Raymond, J.W., Blankley, C.J. and Willett, P. (2003) Comparison of chemical clustering methods using graph- and fingerprint-based similarity measures. Journal of Molecular Graphics and Modelling, 21 (5). pp. 421-433. ISSN 1093-3263
Abstract
This paper compares several published methods for clustering chemical structures, using both graph- and fingerprint-based similarity measures. The clusterings from each method were compared to determine the degree of cluster overlap. Each method was also evaluated on how well it grouped structures into clusters possessing a non-trivial substructural commonality. The methods which employ adjustable parameters were tested to determine the stability of each parameter for datasets of varying size and composition. Our experiments suggest that both graph- and fingerprint-based similarity measures can be used effectively for generating chemical clusterings; it is also suggested that the CAST and Yin–Chen methods, suggested recently for the clustering of gene expression patterns, may also prove effective for the clustering of 2D chemical structures.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | Copyright © 2002 Elsevier Science Inc. This is an author produced electronic version of an article accepted for publication in Journal of Molecular Graphics and Modelling. |
Keywords: | Bit-string; Chemical graph; Chemical series; Clustering method; Fingerprint; Maximum common subgraph; Molecular similarity; Similarity |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > Information School (Sheffield) The University of Sheffield > University of Sheffield Research Centres and Institutes > The Krebs Institute for Biomolecular Research (Sheffield) |
Depositing User: | Repository Officer |
Date Deposited: | 15 Nov 2004 |
Last Modified: | 05 Jun 2014 22:44 |
Published Version: | http://www.elsevier.com/locate/JMGM |
Status: | Published |
Refereed: | Yes |
Identification Number: | 10.1016/S1093-3263(02)00188-2 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:170 |