Performance of Machine Learning Methods for Ligand-Based Virtual Screening

Author(s): Dariusz Plewczynski, Stephane A.H. Spieser, Uwe Koch

Journal Name: Combinatorial Chemistry & High Throughput Screening
Accelerated Technologies for Biotechnology, Bioassays, Medicinal Chemistry and Natural Products Research

Volume 12 , Issue 4 , 2009

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Abstract:

Computational screening of compound databases has become increasingly popular in pharmaceutical research. This review focuses on the evaluation of ligand-based virtual screening using active compounds as templates in the context of drug discovery. Ligand-based screening techniques are based on comparative molecular similarity analysis of compounds with known and unknown activity. We provide an overview of publications that have evaluated different machine learning methods, such as support vector machines, decision trees, ensemble methods such as boosting, bagging and random forests, clustering methods, neuronal networks, naïve Bayesian, data fusion methods and others.

Keywords: QSAR, machine learning, virtual screening, drug discovery

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Article Details

VOLUME: 12
ISSUE: 4
Year: 2009
Page: [358 - 368]
Pages: 11
DOI: 10.2174/138620709788167962
Price: $65

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