Willett, P. (2006) Enhancing the effectiveness of ligand-based virtual screening using data fusion. QSAR & Combinatorial Science, 25 (12). pp. 1143-1152. ISSN 1611-020X
Abstract
Data fusion is being increasingly used to combine the outputs of different types of sensor. This paper reviews the application of the approach to ligand-based virtual screening, where the sensors to be combined are functions that score molecules in a database on their likelihood of exhibiting some required biological activity. Much of the literature to date involves the combination of multiple similarity searches, although there is also increasing interest in the combination of multiple machine learning techniques. Both approaches are reviewed here, focusing on the extent to which fusion can improve the effectiveness of searching when compared with a single screening mechanism, and on the reasons that have been suggested for the observed performance enhancement.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2006 WILEY-VCH Verlag GmbH&Co. This is an author produced version of a paper published in QSAR & Combinatorial Science. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | consensus scoring, data fusion, fusion rule, ligand docking, machine learning, scoring function, similarity searching, similarity-based virtual screening, structure-based virtual screening |
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: | 29 Jan 2008 12:03 |
Last Modified: | 08 Feb 2013 16:55 |
Published Version: | http://dx.doi.org/10.1002/qsar.200610084 |
Status: | Published |
Publisher: | Wiley |
Refereed: | Yes |
Identification Number: | 10.1002/qsar.200610084 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:3608 |