Navigating high-dimensional activity landscapes: design and application of the ligand-target differentiation map.
The transformation of high-dimensional bioactivity spaces into activity landscape representations is as of yet an unsolved problem in computational medicinal chemistry. High-dimensional activity spaces result from the experimental evaluation of compound sets on large numbers of targets. We introduce a first concept to represent and navigate high-dimensional activity landscapes that is based on a data structure termed ligand-target differentiation (LTD) map. This approach is designed to reduce the complexity of high-dimensional bioactivity spaces and enable the identification and further analysis of compound subsets with interesting activity and structural relationships. Its utility has been demonstrated using a set of more than 1400 inhibitors with exact activity measurements for varying numbers of 172 kinases.
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.
Preeti
P
Iyer
Dilyana
D
Dimova
Martin
M
Vogt
Jürgen
J
Bajorath
D003196:Computer Graphics; D055808:Drug Discovery / Q000379:methods*; D008024:Ligands; D058990:Molecular Targeted Therapy; D047428:Protein Kinase Inhibitors / Q000737:chemistry / Q000378:metabolism / Q000494:pharmacology; D011494:Protein Kinases / Q000378:metabolism; D013329:Structure-Activity Relationship; D013379:Substrate Specificity
D008024:Ligands; D047428:Protein Kinase Inhibitors; D011494:Protein Kinases