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Combination of a naive Bayes classifier with consensus scoring improves enrichment of high-throughput docking results.

Klon, Anthony and Glick, Meir and Davies, John (2004) Combination of a naive Bayes classifier with consensus scoring improves enrichment of high-throughput docking results. Journal of Medicinal Chemistry, 47 (18). pp. 4356-4359. ISSN 0022-2623

Abstract

We have previously shown that a machine learning technique can improve the enrichment of high-throughput docking (HTD) results. In the previous cases studied, however, the application of a naive Bayes classifier failed to improve enrichment for instances where HTD alone was unable to generate an acceptable enrichment. We present here a protocol to rescue poor docking results a priori using a combination of rank-by-median consensus scoring and naive Bayesian categorization.

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Date Deposited: 14 Dec 2009 14:07
Last Modified: 31 Jan 2013 01:31
URI: https://oak.novartis.com/id/eprint/45

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