A method for visualizing recurrent topological substructures in sets of active molecules

We present a method for detecting meaningful common topological substructures among sets of active compounds. A clique-based subgraph detection method is used to find the highest scoring-common substructure (HSCS) for each pair of molecules. Only those HSCSs are kept that have a much larger score than would be expected by chance for a randomly selected pair of molecules df the same size. Information on these significant HSCSs is visualized in two complementary-ways; Individual HSCSs can be displayed as pairs of molecules with the atoms in the HSCS highlighted. The HSCSs are presented in order of decreasing statistical significance. Alternatively, individual molecules can be displayed such that each atom is labeled with the number of significant HSCSs in which it has appeared. These are presented in order of decreasing maximum number per molecule. Browsing these data gives an impression of what parts of molecules are conserved among actives. The molecules can be simplified by deleting parts that are not conserved. We show three examples taken from the MDDR database. One example highlights common substructures among diverse CCK antagonists. Two examples point out substructures common between actives on different receptors: benzodiazepine agonists vs CCK antagonists and antidepressants vs antihistamines.