Deciphering a Pharmacophore Network: A Case Study Using BCR-ABL Data

This
paper introduces a general method that can be used to create
groups of pharmacophores to support their further in-depth analysis.
A BCR-ABL molecular dataset was used to calculate graph edit distances
between pharmacophores and led to their organization into a novel
pharmacophore network. The application of a graph layout algorithm
allowed us to discriminate between the pharmacophores associated with
active compounds and those associated with inactive compounds. A clustering
approach was used to refine the partitioning by grouping the pharmacophores
based on their structures, activities, and binding modes. Analysis
of a newly spatialized pharmacophore network provided us with critical
insight into structure–activity relationships, most notably
those that revealed distinctions between activity classes and chemical
families. As shown, this method permits us to identify families of
structurally homogeneous pharmacophores.