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