Authors:
Martin Gronemann
1
;
Michael Jünger
1
;
Nils Kriege
2
and
Petra Mutzel
2
Affiliations:
1
Universität zu Köln, Germany
;
2
Technische Universität Dortmund, Germany
Keyword(s):
Graph Drawing, Clustered Graphs, Topographic Maps, Drug Discovery, Molecule Libraries.
Related
Ontology
Subjects/Areas/Topics:
Abstract Data Visualization
;
Computer Vision, Visualization and Computer Graphics
;
General Data Visualization
;
Graph Visualization
;
Information and Scientific Visualization
;
Visualization Algorithms and Technologies
;
Visualization Applications
Abstract:
We present a new application for graph drawing and visualization in the context of drug discovery. Combining the scaffold-based cluster hierarchy with molecular similarity graphs—both standard concepts in cheminformatics — allows one to get new insights for analyzing large molecule libraries. The derived clustered graphs represent different aspects of structural similarity. We suggest visualizing them as topographic maps. Since the cluster hierarchy does not reflect the underlying graph structure as in (Gronemann and Jünger, 2012), we suggest a new partitioning algorithm that takes the edges of the graph into account. Experiments show that the new algorithm leads to significant improvements in terms of the edge lengths in the obtained drawings.