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Combination rules for group fusion in similarity-based virtual screening

Chen, B.N., Mueller, C. and Willett, P. (2010) Combination rules for group fusion in similarity-based virtual screening. Molecular Informatics , 29 (6-7). pp. 533-541. ISSN 1868-1743

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Abstract

This paper evaluates the screening effectiveness of 15 parameter-free, similarity-based and rank-based rules for group fusion, where one combines the outputs of similarity searches from multiple reference structures using ECFC_4 fingerprints and a Bayesian inference network. Searches of the MDDR and WOMBAT databases show that group fusion is most effective when as many reference structures as possible are used, when only a small proportion of each ranked similarity list is submitted to the final fusion rule, and when a fusion rule based on reciprocal rank positions is used to combine the individual search outputs. An analysis of the reciprocal rank rule suggests that its effectiveness derives from the close relationship that exists between the reciprocal rank of a database structure and its probability of activity.

Item Type: Article
Keywords: Data fusion; Drug discovery; Fusion rule; Group fusion; Virtual screening
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: 01 Jun 2011 09:35
Last Modified: 21 Nov 2013 09:33
Published Version: http://dx.doi.org/10.1002/minf.201000050
Status: Published
Publisher: Wiley
Identification Number: 10.1002/minf.201000050
URI: http://eprints.whiterose.ac.uk/id/eprint/43050

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