Abstract
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
Current Topics in Medicinal Chemistry
Title: Decision Tree Methods in Pharmaceutical Research
Volume: 6 Issue: 1
Author(s): Paul E. Blower and Kevin P. Cross
Affiliation:
Keywords: combinatorial chemistry, RECURSIVE PARTITIONING, pharmacophores, Sequential screening, SAR, Decision Forest (DF), Molconn-X topological descriptors
Abstract: 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.
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Cite this article as:
Blower E. Paul and Cross P. Kevin, Decision Tree Methods in Pharmaceutical Research, Current Topics in Medicinal Chemistry 2006; 6 (1) . https://dx.doi.org/10.2174/156802606775193301
DOI https://dx.doi.org/10.2174/156802606775193301 |
Print ISSN 1568-0266 |
Publisher Name Bentham Science Publisher |
Online ISSN 1873-4294 |
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