STITCH: interaction networks of chemicals and proteins

By Michael Kuhn, C. von Mering, Monica Campillos, Lars Juhl Jensen, Peer Bork. Nucleic Acids Res., 2007

937 citations recorded by the source corpus.

The knowledge about interactions between proteins and small molecules is essential for the understanding of molecular and cellular functions. However, information on such interactions is widely dispersed across numerous databases and the literature. To facilitate access to this data, STITCH (‘search tool for interactions of chemicals’) integrates information about interactions from metabolic pathways, crystal structures, binding experiments and drug–target relationships. Inferred information from phenotypic effects, text mining and chemical structure similarity is used to predict relations between chemicals. STITCH further allows exploring the network of chemical relations, also in the context of associated binding proteins. Each proposed interaction can be traced back to the original data sources. Our database contains interaction information for over 68 000 different chemicals, including 2200 drugs, and connects them to 1.5 million genes across 373 genomes and their interactions contained in the STRING database. STITCH is available at http://stitch.embl.de/

Subfields: Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/b063497861bf8c3e5492930e03dd39352833261c

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Citations in the corpus

Machine learning approaches and databases for prediction of drug–target interaction: a survey paper (2020, methodology): “STITCH STITCH [248–252] is a database that stores information for interactions between proteins and small molecules.”
The Comparative Toxicogenomics Database: update 2011 (2010, background): “The value and utility of CTD is evidenced by it being indexed at numerous other databases, including PubChem (6), PharmGKB (7), UniProt (8), T3DB (9), GAD (10) and ChemID (11), and by the inclusion of CTD’s curated information in other database products, such as STITCH (12), ToppGene (13), PhenoHM (”
TCMID: traditional Chinese medicine integrative database for herb molecular mechanism analysis (2012, methodology): “We used the herbal ingredients’ general names and other alternative names to search STITCH and retrieved the related targets (protein); we then converted the corresponding target’s id into UniProt AC for unification purpose.”
STITCH 3: zooming in on protein–chemical interactions (2011, methodology): “When preparing the ﬁrst release of STITCH in 2007 (3), we decided to merge different salt forms and stereoisomers of active molecules to combine as much information as possible (Figure 3A).”
Computational tools for prioritizing candidate genes: boosting disease gene discovery (2012, background): “Thus, prioritization schemes can potentially be developed for alternative transcripts and protein isoforms, peptides, non-coding RNAs, metabolites and drugs 120 .”
BATMAN-TCM: a Bioinformatics Analysis Tool for Molecular mechANism of Traditional Chinese Medicine (2016, methodology): “1) which were computed based both on co-occurrence in literature and on nat-ural language processing 48 .”
Comparative Toxicogenomics Database: a knowledgebase and discovery tool for chemical–gene–disease networks (2008, background): “Many public chemical databases, such as PharmGKB (8), DrugBank (9), ChemBank (10) and STITCH (11) focus on drugs and other small molecules, providing an invaluable resource for therapeutic research.”