Chen, B., Harrison, R.F., Papadatos, G. et al. (5 more authors) (2007) Evaluation of machine-learning methods for ligand-based virtual screening. Journal of Computer-Aided Molecular Design, 21 (1-3). 53 - 62.
Abstract
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 realvalued 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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2007 Kluwer Academic Publishers. This is an author produced version of a paper subsequently published in Journal of Computer-Aided Molecular Design . Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Group fusion; � Kernel discrimination; � Ligand-based virtual screening; � Machine learning; � Naive Bayesian classifier; � Similarity searching; � Virtual screening |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) The University of Sheffield > Faculty of Science (Sheffield) > Department of Chemistry (Sheffield) The University of Sheffield > Faculty of Social Sciences (Sheffield) > Information School (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 10 Feb 2014 15:35 |
Last Modified: | 14 Feb 2014 11:41 |
Published Version: | http://dx.doi.org/10.1007/s10822-006-9096-5 |
Status: | Published |
Publisher: | Kluwer |
Refereed: | Yes |
Identification Number: | 10.1007/s10822-006-9096-5 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:77599 |