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Virtual Screening Using Binary Kernel Discrimination: Effect of Noisy Training Data and the Optimization of Performance

Record type: Article

Binary kernel discrimination (BKD) uses a training set of compounds, for which structural and qualitative activity data are available, to produce a model that can then be applied to the structures of other compounds in order to predict their likely activity. Experiments with the MDL Drug Data Report database show that the optimal value of the smoothing parameter, and hence the predictive power of BKD, is crucially dependent on the number of false positives in the training set. It is also shown that the best results for BKD are achieved using one particular optimization method for the determination of the smoothing parameter that lies at the heart of the method and using the Jaccard/Tanimoto coefficient in the kernel function that is used to compute the similarity between a test set molecule and the members of the training set.

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Citation

Chen, Beining, Harrison, Robert F., Pasupa, Kitsuchart, Willett, Peter, Wilton, David J. and Wood, David J. (2006) Virtual Screening Using Binary Kernel Discrimination: Effect of Noisy Training Data and the Optimization of Performance Journal of Chemical Information and Modeling, 46, (2), pp. 478-486. (doi:10.1021/ci0505426).

More information

Published date: 21 February 2006
Organisations: Electronics & Computer Science

Identifiers

Local EPrints ID: 266574
URI: http://eprints.soton.ac.uk/id/eprint/266574
ISSN: 1549-9596
PURE UUID: 4c65343a-3d75-4160-b34f-872763231efd

Catalogue record

Date deposited: 14 Aug 2008 12:48
Last modified: 18 Jul 2017 07:15

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Contributors

Author: Beining Chen
Author: Robert F. Harrison
Author: Kitsuchart Pasupa
Author: Peter Willett
Author: David J. Wilton
Author: David J. Wood

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