Composition and topology of activity cliff clusters formed by bioactive compounds.
The assessment of activity cliffs has thus far mostly focused on compound pairs, although the majority of activity cliffs are not formed in isolation but in a coordinated manner involving multiple active compounds and cliffs. However, the composition of coordinated activity cliff configurations and their topologies are unknown. Therefore, we have identified all activity cliff configurations formed by currently available bioactive compounds and analyzed them in network representations where activity cliff configurations occur as clusters. The composition, topology, frequency of occurrence, and target distribution of activity cliff clusters have been determined. A limited number of large cliff clusters with unique topologies were identified that were centers of activity cliff formation. These clusters originated from a small number of target sets. However, most clusters were of small to moderate size. Three basic topologies were sufficient to describe recurrent activity cliff cluster motifs/topologies. For example, frequently occurring clusters with star topology determined the scale-free character of the global activity cliff network and represented a characteristic activity cliff configuration. Large clusters with complex topology were often found to contain different combinations of basic topologies. Our study provides a first view of activity cliff configurations formed by currently available bioactive compounds and of the recurrent topologies of activity cliff clusters. Activity cliff clusters of defined topology can be selected, and from compounds forming the clusters, SAR information can be obtained. The SAR information of activity cliff clusters sharing a/one specific activity and topology can be compared.
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.
Dagmar
D
Stumpfe
Dilyana
D
Dimova
Jürgen
J
Bajorath
D016000:Cluster Analysis; D019295:Computational Biology; D055808:Drug Discovery; D006801:Humans; D013329:Structure-Activity Relationship