title: Comparison of bioactive chemical space networks generated using substructure- and fingerprint-based measures of molecular similarity.
abstract: Chemical space networks (CSNs) have recently been introduced as a conceptual alternative to coordinate-based representations of chemical space. CSNs were initially designed as threshold networks using the Tanimoto coefficient as a continuous similarity measure. The analysis of CSNs generated from sets of bioactive compounds revealed that many statistical properties were strongly dependent on their edge density. While it was difficult to compare CSNs at pre-defined similarity threshold values, CSNs with constant edge density were directly comparable. In the current study, alternative CSN representations were constructed by applying the matched molecular pair (MMP) formalism as a substructure-based similarity criterion. For more than 150 compound activity classes, MMP-based CSNs (MMP-CSNs) were compared to corresponding threshold CSNs (THR-CSNs) at a constant edge density by applying different parameters from network science, measures of community structure distributions, and indicators of structure-activity relationship (SAR) information content. MMP-CSNs were found to be an attractive alternative to THR-CSNs, yielding low edge densities and well-resolved topologies. MMP-CSNs and corresponding THR-CSNs often had similar topology and closely corresponding community structures, although there was only limited overlap in similarity relationships. The homophily principle from network science was shown to affect MMP-CSNs and THR-CSNs in different ways, despite the presence of conserved topological features. Moreover, activity cliff distributions in alternative CSN designs markedly differed, which has important implications for SAR analysis.
authors.affiliation: Department of Life Science Informatics, B-IT, LIMES Program Unit Chemical Biology and Medicinal Chemistry, Rheinische Friedrich-Wilhelms-Universität, Dahlmannstr. 2, 53113, Bonn, Germany.
authors.forename: Bijun
authors.initials: B
authors.lastname: Zhang
authors.forename: Martin
authors.initials: M
authors.lastname: Vogt
authors.forename: Gerald M
authors.initials: GM
authors.lastname: Maggiora
authors.forename: Jürgen
authors.initials: J
authors.lastname: Bajorath
mesh_terms: D016000:Cluster Analysis; D003196:Computer Graphics; D053509:Matrix Metalloproteinase 13 / Q000737:chemistry / Q000378:metabolism; D008956:Models, Chemical; D008958:Models, Molecular; D015233:Models, Statistical; D043783:Receptor, Bradykinin B1 / Q000737:chemistry / Q000378:metabolism; D013329:Structure-Activity Relationship
chemical_list: D043783:Receptor, Bradykinin B1; D053509:Matrix Metalloproteinase 13