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Combinatorial Chemistry & High Throughput Screening

Editor-in-Chief

ISSN (Print): 1386-2073
ISSN (Online): 1875-5402

Machine Learning in Virtual Screening

Author(s): James L. Melville, Edmund K. Burke and Jonathan D. Hirst

Volume 12, Issue 4, 2009

Page: [332 - 343] Pages: 12

DOI: 10.2174/138620709788167980

Price: $65

Abstract

In this review, we highlight recent applications of machine learning to virtual screening, focusing on the use of supervised techniques to train statistical learning algorithms to prioritize databases of molecules as active against a particular protein target. Both ligand-based similarity searching and structure-based docking have benefited from machine learning algorithms, including naïve Bayesian classifiers, support vector machines, neural networks, and decision trees, as well as more traditional regression techniques. Effective application of these methodologies requires an appreciation of data preparation, validation, optimization, and search methodologies, and we also survey developments in these areas.

Keywords: Machine learning, virtual screening, data mining, drug discovery


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