Decision Tree Methods in Pharmaceutical Research

Author(s): Paul E. Blower, Kevin P. Cross

Journal Name: Current Topics in Medicinal Chemistry

Volume 6 , Issue 1 , 2006

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Decision trees are among the most popular of the new statistical learning methods being used in the pharmaceutical industry for predicting quantitative structure-activity relationships. This article reviews applications of decision trees in drug discovery research and extensions to the basic algorithm using hybrid or ensemble methods that improve prediction accuracy.

Keywords: combinatorial chemistry, RECURSIVE PARTITIONING, pharmacophores, Sequential screening, SAR, Decision Forest (DF), Molconn-X topological descriptors

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Article Details

Year: 2006
Page: [31 - 39]
Pages: 9
DOI: 10.2174/156802606775193301

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