title: Extraction of discontinuous structure-activity relationships from compound data sets through particle swarm optimization.
abstract: The characterization of structure-activity relationship (SAR) features of large compound data sets has been a hot topic in recent years, and different methods for large-scale SAR analysis have been introduced. The exploration of local SAR components and prioritization of compound subsets have thus far mostly relied on graphical analysis methods that capture similarity and potency relationships in a systematic manner. A currently unsolved problem in large-scale SAR analysis is how to automatically select those compound subsets from large data sets that carry most SAR information. For this purpose, we introduce a numerical optimization scheme that is based on particle swarm optimization guided by an SAR scoring function. The methodology is applied to four large compound sets. We demonstrate that compound subsets representing the most discontinuous local SARs are consistently selected through particle swarm optimization.
authors.affiliation: Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstr. 2, D-53113 Bonn, Germany.
authors.forename: Vigneshwaran
authors.initials: V
authors.lastname: Namasivayam
authors.forename: Preeti
authors.initials: P
authors.lastname: Iyer
authors.forename: Jürgen
authors.initials: J
authors.lastname: Bajorath
mesh_terms: D000818:Animals; D019295:Computational Biology; D003198:Computer Simulation; D004791:Enzyme Inhibitors / Q000737:chemistry*; D006801:Humans; D015394:Molecular Structure; D054852:Small Molecule Libraries; D013329:Structure-Activity Relationship
chemical_list: D004791:Enzyme Inhibitors; D054852:Small Molecule Libraries