title: Feature trees: a new molecular similarity measure based on tree matching.
abstract: In this paper we present a new method for evaluating molecular similarity between small organic compounds. Instead of a linear representation like fingerprints, a more complex description, a feature tree, is calculated for a molecule. A feature tree represents hydrophobic fragments and functional groups of the molecule and the way these groups are linked together. Each node in the tree is labeled with a set of features representing chemical properties of the part of the molecule corresponding to the node. The comparison of feature trees is based on matching subtrees of two feature trees onto each other. Two algorithms for tackling the matching problem are described throughout this paper. On a dataset of about 1000 molecules, we demonstrate the ability of our approach to identify molecules belonging to the same class of inhibitors. With a second dataset of 58 molecules with known binding modes taken from the Brookhaven Protein Data Bank, we show that the matchings produced by our algorithms are compatible with the relative orientation of the molecules in the active site in 61% of the test cases. The average computation time for a pair comparison is about 50 ms on a current workstation.
authors.affiliation: German National Research Center for Information Technology (GMD), Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany. Rarey@gmd.de
authors.forename: M
authors.initials: M
authors.lastname: Rarey
authors.forename: J S
authors.initials: JS
authors.lastname: Dixon
mesh_terms: D000465:Algorithms; D003628:Database Management Systems; D011506:Proteins / Q000737:chemistry*
chemical_list: D011506:Proteins