CHEMGENIE: integration of chemogenomics data for applications in chemical biology. Issue 1 (January 2018)

• Record Type: Journal Article
• Authors: Kutchukian, Peter S.; Chang, Charlie; Fox, Sean J.; Cook, Erica; Barnard, Richard; Tellers, David; Wang, Huijun; Pertusi, Dante; Glick, Meir; Sheridan, Robert P.; Wallace, Iain M.; Wassermann, Anne Mai
• Is Part Of: Drug discovery today. Volume 23:Issue 1(2018)
• Page Start: 151
• Page End: 160
• Publication Date: 2018-01
• Subjects: Drugs -- Design -- Periodicals; Drugs -- Research -- Periodicals; 615.1
• DOI: 10.1016/j.drudis.2017.09.004
• Languages: English
• ISSNs: 1359-6446

Abstract

Highlights: Ongoing efforts at our company to integrate and structure chemogenomics data are described. The usefulness of a chemogenomics database for common applications in drug discovery is highlighted. Applications such as focused library design, model building and MoA deconvolution are shown. Common caveats and pitfalls in capturing and interpreting chemogenomics data are discussed. Abstract : Increasing amounts of biological data are accumulating in the pharmaceutical industry and academic institutions. However, data does not equal actionable information, and guidelines for appropriate data capture, harmonization, integration, mining, and visualization need to be established to fully harness its potential. Here, we describe ongoing efforts at Merck & Co. to structure data in the area of chemogenomics. We are integrating complementary data from both internal and external data sources into one chemogenomics database (Chemical Genetic Interaction Enterprise; CHEMGENIE). Here, we demonstrate how this well-curated database facilitates compound set design, tool compound selection, target deconvolution in phenotypic screening, and predictive model building.