White Rose University Consortium logo
University of Leeds logo University of Sheffield logo York University logo

Evaluation of machine-learning methods for ligand-based virtual screening

Chen, B., Harrison, R., Papadatos, G., Willett, P., Wood, D., Lewell, X., Greenidge, P. and Stiefl, N. (2007) Evaluation of machine-learning methods for ligand-based virtual screening. Journal of Computer-Aided Molecular Design, 21 (1-3). pp. 53-62. ISSN 0920-654X

[img] Text

Download (235Kb)


Machine-learning methods can be used for virtual screening by analysing the structural characteristics of molecules of known (in)activity, and we here discuss the use of kernel discrimination and naive Bayesian classifier (NBC) methods for this purpose. We report a kernel method that allows the processing of molecules represented by binary, integer and real-valued descriptors, and show that it is little different in screening performance from a previously described kernel that had been developed specifically for the analysis of binary fingerprint representations of molecular structure. We then evaluate the performance of an NBC when the training-set contains only a very few active molecules. In such cases, a simpler approach based on group fusion would appear to provide superior screening performance, especially when structurally heterogeneous datasets are to be processed.

Item Type: Article
Copyright, Publisher and Additional Information: © 2007 Springer. This is an author produced version of a paper subsequently published in Journal Title. Uploaded in accordance with the publisher's self-archiving policy.
Keywords: group fusion, kernel discrimination, lLigand-based virtual screening, machine learning, naive Bayesian classifier, similarity searching, virtual screening
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Social Sciences (Sheffield) > Information School (Sheffield)
Depositing User: Repository Officer
Date Deposited: 09 Jan 2008 15:34
Last Modified: 08 Feb 2013 16:55
Published Version: http://dx.doi.org/10.1007/s10822-006-9096-5
Status: Published
Publisher: Springer
Refereed: Yes
Identification Number: 10.1007/s10822-006-9096-5
URI: http://eprints.whiterose.ac.uk/id/eprint/3557

Actions (repository staff only: login required)