title: Local structural changes, global data views: graphical substructure-activity relationship trailing.
abstract: The systematic extraction of structure-activity relationship (SAR) information from large and diverse compound data sets depends on the application of computational analysis methods. Irrespective of the methodological details, the ultimate goal of large-scale SAR analysis is to identify most informative compounds and rationalize structural changes that determine SAR behavior. Such insights provide a basis for further chemical exploration. Herein we introduce the first graphical SAR analysis method that globally organizes large compound data sets on the basis of local structural relationships, hence providing an immediate access to important structural modifications and SAR determinants.
authors: Department of Life Science Informatics, Rheinische Friedrich-Wilhelms-Universität, Bonn, Germany.
authors: Mathias
authors: M
authors: Wawer
authors: Jürgen
authors: J
authors: Bajorath
mesh_terms: D000465:Algorithms; D003196:Computer Graphics; D013329:Structure-Activity Relationship