title: Decision tree methods in pharmaceutical research.
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.
authors.affiliation: Leadscope, Inc., 1393 Dublin Road, Columbus, OH 43215, USA. pblower@leadscope.com
authors.forename: Paul E
authors.initials: PE
authors.lastname: Blower
authors.forename: Kevin P
authors.initials: KP
authors.lastname: Cross
mesh_terms: D002626:Chemistry, Pharmaceutical / Q000379:methods*; D003663:Decision Trees; D015195:Drug Design; D006801:Humans; D021281:Quantitative Structure-Activity Relationship