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SuperTarget and Matador: resources for exploring drug-target relationships
Open accessAbstract
The molecular basis of drug action is often not well understood. This is partly because the very abundant and diverse information generated in the past decades on drugs is hidden in millions of medical articles or textbooks. Therefore, we developed a one-stop data warehouse, SuperTarget that integrates drug-related information about medical indication areas, adverse drug effects, drug metabolization, pathways and Gene Ontology terms of the target proteins. An easy-to-use query interface enables the user to pose complex queries, for example to find drugs that target a certain pathway, interacting drugs that are metabolized by the same cytochrome P450 or drugs that target the same protein but are metabolized by different enzymes. Furthermore, we provide tools for 2D drug screening and sequence comparison of the targets. The database contains more than 2500 target proteins, which are annotated with about 7300 relations to 1500 drugs; the vast majority of entries have pointers to the respective literature source. A subset of these drugs has been annotated with additional binding information and indirect interactions and is available as a separate resource called Matador. SuperTarget and Matador are available at http://insilico.charite.de/supertarget and http://matador.embl.de.
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“This analysis identified 10 drug annotations with FDR < 0.05 including four drugs that are either estrogen receptor agonists (diethylstilbestrol, Implanon [etonogestril implant]), or anti-estrogens (tamoxifen and raloxifene); in addition to nicotine, cocaine, cyclothiazide, felbamate, and riluzole.”
From Bi-ancestral depression GWAS in the Million Veteran Program and meta-analysis in >1.2 million individuals highlight new therapeutic directions (2021) · cites this paper as [19]
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Cites
DrugBank: a comprehensive resource for in silico drug discovery and exploration
Wishart, D. S. (2006) · Nucleic Acids Research · doi:10.1093/nar/gkj067 · Cited by 3,892 · cited 4×
“In a last step, relations from other databases, namely DrugBank (3), KEGG (9), PDB (10), SuperLigands (11) and TTD (4), were checked for drug-target interactions not identified with the preceding steps.”
BackgroundInfluentialThe Universal Protein Resource (UniProt)
Bairoch, A. (2004) · Nucleic Acids Research · doi:10.1093/nar/gki070 · Cited by 3,774
From genomics to chemical genomics: new developments in KEGG
Kanehisa, M. I. (2005) · Nucleic Acids Research · doi:10.1093/nar/gkj102 · Cited by 3,126 · cited 2×
“Proven or potential target proteins are represented by sequences as stored in UniProt (12), by functional annotations extracted from GOA (13) and by related pathway information provided by KEGG (9) (compare Figure 1).”
Backgroundvon Mering, C. (2004) · Nucleic Acids Research · doi:10.1093/nar/gki005 · Cited by 1,768 · cited 2×
“The most probable candidates were identified using a benchmarking scheme ( 8 ) and manually curated.”
MethodThe Gene Ontology Annotation (GOA) Database: sharing knowledge in Uniprot with Gene Ontology
Camon, E. B. (2003) · Nucleic Acids Research · doi:10.1093/nar/gkh021 · Cited by 927 · cited 1×
“Proven or potential target proteins are represented by sequences as stored in UniProt (12), by functional annotations extracted from GOA (13) and by related pathway information provided by KEGG (9) (compare Figure 1).”
BackgroundDrugs, their targets and the nature and number of drug targets
Imming, P., Sinning, C., & Meyer, A. (2006) · Nature Reviews Drug Discovery · doi:10.1038/nrd2132 · Cited by 850 · cited 1×
“For some drugs even the primary targets are still unknown, for example, Diloxanide, Niclosamide and Ambroxol are administered successfully although their effect on human metabolism is still not clarified at a molecular level (1).”
BackgroundDo Structurally Similar Molecules Have Similar Biological Activity?
Martin, Y. C., Kofron, J. L., & Traphagen, L. M. (2002) · Journal of Medicinal Chemistry · doi:10.1021/jm020155c · Cited by 806 · cited 1×
“Since compounds with high structural similarity frequently exhibit similar activities (14), all drugs with comparable structural fingerprints are made accessible.”
BackgroundTTD: Therapeutic Target Database
Chen, X. (2002) · Nucleic Acids Research · doi:10.1093/nar/30.1.412 · Cited by 723 · cited 4×
“Another notable database is the Therapeutic Target Database (TTD) (4), which holds target information on about 1000 small molecule drugs.”
BackgroundInfluentialLiterature mining for the biologist: from information retrieval to biological discovery
Jensen, L. J., Šarić, J., & Peer Bork, B. (2006) · Nature Reviews Genetics · doi:10.1038/nrg1768 · Cited by 632 · cited 1×
“In parallel, potential drug-target relations were automatically extracted from Medline by searching for synonyms of drugs, proteins and Medical Subject Headings (MeSH terms) describing groups of proteins (7).”
MethodSelf-citationThe Universal Protein Resource (UniProt)
The UniProt Consortium (2006) · Nucleic Acids Research · doi:10.1093/nar/gkl929 · Cited by 572
Steinbeck, C., Hoppe, C., Kühn, S., Floris, M., Guha, R., & Willighagen, E. (2006) · Current Pharmaceutical Design · doi:10.2174/138161206777585274 · Cited by 463 · cited 1×
“Structural similarity scores and fingerprints are computed with the open-source Chemistry Development Kit (15).”
MethodDeshpande, N. (2004) · Nucleic Acids Research · doi:10.1093/nar/gki057 · Cited by 341 · cited 3×
“In a last step, relations from other databases, namely DrugBank (3), KEGG (9), PDB (10), SuperLigands (11) and TTD (4), were checked for drug-target interactions not identified with the preceding steps.”
BackgroundInfluentialFrantz, S. (2005) · Nature · doi:10.1038/437942a · Cited by 322 · cited 1×
“Even if the medical effect has been explained by a certain molecular interaction, most drugs interact with several additional targets, which may either strengthen the therapeutic effect or cause unwanted adverse drug effects ( 2 ).”
BackgroundEBIMed—text crunching to gather facts for proteins from Medline
Rebholz‐Schuhmann, D., Kirsch, H., Arregui, M., Gaudan, S., Riethoven, J.-J. M., & Stoehr, P. J. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btl302 · Cited by 217 · cited 1×
“Starting with 2400 drugs and their synonyms from the SuperDrug Database (5), the text mining tool EbiMed (6) was used to extract relevant text passages containing potential drug-target relations from about 15 millions public abstracts listed in PubMed.”
MethodCytochrome P450 and Anticancer Drugs
Fujita, K. (2005) · Current Drug Metabolism · doi:10.2174/138920006774832587 · Cited by 90 · cited 1×
“Genetic polymorphism of cytochrome P450 genes or associated regulatory factors may lead to diverse ability of drug degradation (17).”
BackgroundSuperDrug: a conformational drug database
Goede, A., Dunkel, M., Mester, N., Frömmel, C., & Preißner, R. (2005) · Computer applications in the biosciences · doi:10.1093/bioinformatics/bti295 · Cited by 76 · cited 1×
“Starting with 2400 drugs and their synonyms from the SuperDrug Database (5), the text mining tool EbiMed (6) was used to extract relevant text passages containing potential drug-target relations from about 15 millions public abstracts listed in PubMed.”
MethodSelf-citationSIMAP: the similarity matrix of proteins
Rattei, T. (2005) · Nucleic Acids Research · doi:10.1093/nar/gkj106 · Cited by 52 · cited 1×
“Analogously, similar proteins are quickly identifiable by precomputed sequence alignments provided by SIMAP (16).”
BackgroundSuperLigands – a database of ligand structures derived from the Protein Data Bank
Michalsky, E., Dunkel, M., Goede, A., & Preißner, R. (2005) · BMC Bioinformatics · doi:10.1186/1471-2105-6-122 · Cited by 41 · cited 2×
“In a last step, relations from other databases, namely DrugBank (3), KEGG (9), PDB (10), SuperLigands (11) and TTD (4), were checked for drug-target interactions not identified with the preceding steps.”
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Network pharmacology: the next paradigm in drug discovery
Hopkins, A. L. (2008) · Nature Chemical Biology · doi:10.1038/nchembio.118 · Cited by 4,622
A comprehensive map of molecular drug targets
Santos, R., Ursu, O., Gaulton, A., Bento, A. P., Donadi, R. S., Bologa, C. G., Karlsson, A., Al-Lazikani, B., Hersey, A., Oprea, T. I., et al. (2017) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2016.230 · Cited by 2,427
“DrugBank 9 , the most widely used specialist drug information resource, maps drugs to proteins that have been reported to bind to them, and SuperTarget 10 is a text-mining-based compilation of direct and indirect drug targets.”
BackgroundDrugBank 3.0: a comprehensive resource for 'Omics' research on drugs
Knox, C., Law, V., Jewison, T., Liu, P., Ly, S., Frolkis, A., Pon, A., Banco, K., Mak, C., Neveu, V., et al. (2010) · Nucleic Acids Research · doi:10.1093/nar/gkq1126 · Cited by 1,832
“Over the years, other drug-specific databases have emerged, including PDTD (2), STITCH (3), SuperTarget (4) and the Druggable Genome database (5).”
BackgroundInfluentialSTITCH 5: augmenting protein–chemical interaction networks with tissue and affinity data
Szklarczyk, D., Santos, A., von Mering, C., Jensen, L. J., Bork, P., & Kuhn, M. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv1277 · Cited by 1,740
Self-citationiDEP: an integrated web application for differential expression and pathway analysis of RNA-Seq data
Ge, X., Son, E., & Yao, R. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2486-6 · Cited by 1,677
Metadata
- Authors
- Gunther, S., Kuhn, M., Dunkel, M., Campillos, M., Senger, C., Petsalaki, E., Ahmed, J., Urdiales, E. G., Gewiess, A., Jensen, L. J., Schneider, R., Skoblo, R., Russell, R. B., Bourne, P. E., Bork, P., & Preissner, R.
- Affiliations
- University of California San Diego (US)
- Corresponding author
- Preissner, R.
- ORCID
- Venue
- Nucleic Acids Research
- Publisher
- Oxford University Press (OUP)
- Published
- 2007-12-23
- Volume
- 36
- Issue
- Database
- Pages
- D919-D922
- ISSN
- 0305-1048 (ISSN-L)
- 1362-4962
- PMC
- PMC2238858
- OpenAlex
- W2108069034
- Cited by
- 647
- References
- 18
- Citations by year
- 2026: 13 · 2025: 34 · 2024: 33 · 2023: 42 · 2022: 57 · 2021: 73 · 2020: 57 · 2019: 48 · 2018: 43 · 2017: 40 · 2016: 24 · 2015: 36 · 2014: 22 · 2013: 28 · 2012: 30
- FWCI
- 16.70
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Nucleic Acids Research (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- MDC Repository (Max-Delbrueck-Center for Molecular Medicine) (acceptedVersion)
- Open Repository and Bibliography (University of Luxembourg) (submittedVersion, cc-by-nc-sa)
- Funders
- Deutsche Forschungsgemeinschaft (SFB 449)
- Bundesministerium für Bildung und Forschung (SFB 449)
- Deutsche Krebshilfe
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Health Sciences › Medicine › Health Informatics › Biomedical Text Mining and Ontologies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- drug-target interactions, matadors, cytochrome P450, drug pathways, Gene Ontology, adverse drug reactions, drug screening, protein sequence comparison, literature annotation
- MeSH terms
- Pharmaceutical Preparations, Pharmacology, Proteins, User-Computer Interface, Drug Design, Databases, Factual, Drug Delivery Systems, Internet
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