title: Automated pharmacophore identification for large chemical data sets.
abstract: The identification of three-dimensional pharmacophores from large, heterogeneous data sets is still an unsolved problem. We developed a novel program, SCAMPI (statistical classification of activities of molecules for pharmacophore identification), for this purpose by combining a fast conformation search with recursive partitioning, a data-mining technique, which can easily handle large data sets. The pharmacophore identification process is designed to run recursively, and the conformation spaces are resampled under the constraints of the evolving pharmacophore model. This program is capable of deriving pharmacophores from a data set of 1000-2000 compounds, with thousands of conformations generated for each compound and in less than 1 day of computational time. For two test data sets, the identified pharmacophores are consistent with the known results from the literature.
authors.affiliation: Chemoinformatics Group, Glaxo Wellcome Inc., Research Triangle Park, North Carolina 27709, USA.
authors.forename: X
authors.initials: X
authors.lastname: Chen
authors.forename: A
authors.initials: A
authors.lastname: Rusinko
authors.forename: A
authors.initials: A
authors.lastname: Tropsha
authors.forename: S S
authors.initials: SS
authors.lastname: Young
mesh_terms: D000806:Angiotensin-Converting Enzyme Inhibitors / Q000737:chemistry / Q000494:pharmacology; D020650:Combinatorial Chemistry Techniques; D016208:Databases, Factual; D015195:Drug Design; D008968:Molecular Conformation; D008996:Monoamine Oxidase Inhibitors / Q000737:chemistry / Q000494:pharmacology; D012984:Software; D012985:Software Design; D013329:Structure-Activity Relationship
chemical_list: D000806:Angiotensin-Converting Enzyme Inhibitors; D008996:Monoamine Oxidase Inhibitors