CHEMGENIE: integration of chemogenomics data for applications in chemical biology

The IUPHAR/BPS Guide to PHARMACOLOGY in 2018: updates and expansion to encompass the new guide to IMMUNOPHARMACOLOGY

“…We encourage in-links from other resources, although it is difficult to discern the extent of these unless we are directly informed or cited. A recent survey ( https://blog.guidetopharmacology.org/2016/03/30/collation-and-assessment-of-gtopdb-in-links ) found these were over 20 in number and we also not that we have recently been incorporated into an internal-only resource, the CHEMGENIE database from Merck & Co ( 49 ).…”

Targeting RNA with Small Molecules: Identification of Selective, RNA-Binding Small Molecules Occupying Drug-Like Chemical Space

“…We then further explored two properties of RNA and protein binders that significantly contributed to the PCA: molecular weight and AlogD. Using CHEMGENIE, 10 our company’s biochemical and pharmacogenomic database, we assembled historical protein ALIS binding data for RNA screening library compounds. We then calculated physicochemical properties for each compound in the screening collection and trained naïve Bayesian classification models to identify key differences in feature weights for RNA or protein binding ( Fig.…”

“…First, 42 RNA targets were identified from literature that represented various therapeutic areas and RNA classes, such as bacterial and viral ncRNA elements, mammalian lncRNA, structural elements in the 3′ or 5′ untranslated region (UTR) of mammalian mRNA, G-quadruplexes, domains of ncRNA known to bind to RNA-binding proteins for function, RNA repeat elements, small nucleolar RNA (snoRNA), and noncoding splice variants. Each of these RNA targets was screened against a Diversity Library (~50,000 chemically diverse compounds) and our internal tool compounds 10 (herein called "Functionally Annotated Library"; ~5100 compounds intended for phenotypic screening). This approach was distinct from "library versus library" screening, as done using the Inforna method, in that it did not require knowledge of RNA folding and only required that small molecule libraries for testing meet general compatibility standards for LC-MS detection.…”

Molecular Profiling Reveals a Common Metabolic Signature of Tissue Fibrosis

“…As part of a major effort to identify new suppressors for fibrosis, we carried out high-throughput phenotypic screens against an annotated compound library as previously described. 37 Normal human lung fibroblasts were treated with TGF-b and subsequently incubated with 3 H-proline to monitor collagen production (Figure 4A). The potency of suppressors was determined by 3-point dose titration.…”

“…In this case, we used unique target annotations as the descriptors. In order to do this, we added all target annotations from the CHEMGENIE database to each compound 37 and kept all dose response values with a potency of < 1 uM and a qualifier of ''='' or ''<.'' We also selected all non-numeric target associations, for example, from the PDB.…”