title: Chemotography for multi-target SAR analysis in the context of biological pathways.
abstract: The increasing amount of chemogenomics data, that is, activity measurements of many compounds across a variety of biological targets, allows for better understanding of pharmacology in a broad biological context. Rather than assessing activity at individual biological targets, today understanding of compound interaction with complex biological systems and molecular pathways is often sought in phenotypic screens. This perspective poses novel challenges to structure-activity relationship (SAR) assessment. Today, the bottleneck of drug discovery lies in the understanding of SAR of rich datasets that go beyond single targets in the context of biological pathways, potential off-targets, and complex selectivity profiles. To aid in the understanding and interpretation of such complex SAR, we introduce Chemotography (chemotype chromatography), which encodes chemical space using a color spectrum by combining clustering and multidimensional scaling. Rich biological data in our approach were visualized using spatial dimensions traditionally reserved for chemical space. This allowed us to analyze SAR in the context of target hierarchies and phylogenetic trees, two-target activity scatter plots, and biological pathways. Chemotography, in combination with the Kyoto Encyclopedia of Genes and Genomes (KEGG), also allowed us to extract pathway-relevant SAR from the ChEMBL database. We identified chemotypes showing polypharmacology and selectivity-conferring scaffolds, even in cases where individual compounds have not been tested against all relevant targets. In addition, we analyzed SAR in ChEMBL across the entire Kinome, going beyond individual compounds. Our method combines the strengths of chemical space visualization for SAR analysis and graphical representation of complex biological data. Chemotography is a new paradigm for chemogenomic data visualization and its versatile applications presented here may allow for improved assessment of SAR in biological context, such as phenotypic assay hit lists.
authors.affiliation: Lead Discovery Informatics, Novartis Institutes for Biomedical Research, 250 Massachusetts Ave., Cambridge, MA 02139, USA. eugen.lounkine@novartis.com
authors.forename: Eugen
authors.initials: E
authors.lastname: Lounkine
authors.forename: Peter
authors.initials: P
authors.lastname: Kutchukian
authors.forename: Paula
authors.initials: P
authors.lastname: Petrone
authors.forename: John W
authors.initials: JW
authors.lastname: Davies
authors.forename: Meir
authors.initials: M
authors.lastname: Glick
mesh_terms: D002845:Chromatography; D016000:Cluster Analysis; D062313:Databases, Pharmaceutical; D055808:Drug Discovery; D015394:Molecular Structure; D004364:Pharmaceutical Preparations / Q000737:chemistry* / Q000378:metabolism*; D013329:Structure-Activity Relationship
chemical_list: D004364:Pharmaceutical Preparations