Article

Navigating high-dimensional activity landscapes: design and application of the ligand-target differentiation map.

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.
Journal of Chemical Information and Modeling (impact factor: 4.68). 07/2012; 52(8):1962-9. DOI:10.1021/ci3002765 pp.1962-9
Source: PubMed

ABSTRACT 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.

0 0
 · 
0 Bookmarks
 · 
34 Views
Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.

Keywords

compound sets
 
compound subsets
 
computational medicinal chemistry
 
data structure
 
exact activity measurements
 
experimental evaluation
 
high-dimensional activity landscapes
 
High-dimensional activity spaces result
 
high-dimensional bioactivity spaces
 
large numbers
 
ligand-target differentiation
 
LTD
 

Preeti Iyer