Cited by 651Computational Drug Discovery MethodsBiomedical Text Mining and OntologiesBioinformatics and Genomic Networks

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SuperTarget and Matador: resources for exploring drug-target relationships

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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.·2007·Nucleic Acids Research

DOI 10.1093/nar/gkm862PMC PMC2238858

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Abstract

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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Figure 1. System architecture and number of database entries of SuperTarget. The database contains the complete Uniprot with more than 3 million entries. Beside the targets, drugs and pathways the database provides 23 000 different GO-terms and 30 000 links to protein structures.CC BY-NC · PubMed Central

Figure 2. Example of a complex query: search for an alternative drug to Taxol, which adresses the same target Bcl-2, but is not metabolized by the cytochrome 2C8. Procedure: start to search for the targets and the metabolizing cytochromes of Taxol in the associated categories. The resulting lists contain among others the target Bcl-2 as well as the P450 cytochrome 2C8. Second, forward 2C8 and Bcl-2 into the query term box, combine them using the ‘NOT’ operator and submit the query. The resulting drug list contains two alternative drug candidates for Taxol (Docetaxel and Flupirtine).CC BY-NC · PubMed Central

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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.”
BackgroundInfluential
The 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).”
Background
STRING: known and predicted protein-protein associations, integrated and transferred across organisms
von 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.”
Method
The 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).”
Background
Drugs, 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).”
Background
Do 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.”
Background
TTD: 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.”
BackgroundInfluential
Literature 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-citation
The Universal Protein Resource (UniProt)
The UniProt Consortium (2006) · Nucleic Acids Research · doi:10.1093/nar/gkl929 · Cited by 572

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“added the fraction of experiments performed so far. 3 Results For validation of our method, experiments are performed on four data sets extracted from the KEGG BRITE ([21]), BRENDA [25] , SuperTarget [28] and DrugBank [29] databases, previously described by Yamanishi et al [12] 2. The data set consists of four drug-ta…”
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“, 2004), SUPERTARGET (Günther et al., 2008), and DrugBank (Wishart et al.”
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Method
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“We selected 10 drug-target pairs with the highest predictive score to validate in SuperTarget [23].”
MethodInfluential
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Background
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Liu, B., Pliakos, K., Vens, C., & Tsoumakas, G. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2012.12325 · Cited by 2
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Background
Computational Approaches for Target Inference
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Method
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Ciaglia, T., Napolitano, V., Miranda, M. R., Gioia, D. L., Musella, S., Moriello, A. S., Merciai, F., Sarno, V. D., Vita, S. D., Colarusso, E., et al. (2025) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.5c01344 · Cited by 1
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“These datasets are often complemented by auxiliary similarity information, including chemical structure features, disease similarity derived from MeSH, UMLS, or Disease Ontology, side-effect profiles from SIDER, and target annotations from UniProt, SuperTarget, and related databases [174].”
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Method
Predicting drug protein interactions based on improved support vector data description in unbalanced data
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Identifying the causative proteins of similar side effect pairs to explore the common molecular basis of these side effects
Wang, Y., Chen, X., Liu, L., Chen, Y., Ma, H., Yang, R., & Liu, X. (2015) · Molecular BioSystems · doi:10.1039/c5mb00242g
Combining drug and gene similarity measures for drug-target elucidation
Sackler, B. (2010)
Efficient network based approaches for pattern recognition and knowledge discovery from large and heterogeneous datasets
Zhu, C. (2013) · OhioLink ETD Center (Ohio Library and Information Network)
Mining the biomedical literature
Mihăilă, C., Batista-Navarro, R., Alnazzawi, N., Kontonatsios, G., Korkontzelos, I., Rak, R., Thompson, P., & Ananiadou, S. (2015) · Research Explorer (The University of Manchester)
Personalized and adaptive recommendation
Liu, Y. (2016) · doi:10.32657/10356/66909
A Semantic-based Similarity of Human Drug Target Proteins
(2013) · International Conference on Bioinformatics Models, Methods and Algorithms (BIOINFORMATICS) · doi:10.5220/0004239703000303
Systems Toxicology Approaches
Jeong, H., Rho, S., & Hwang, D. (2013) · CRC Press eBooks · doi:10.1201/b15476-12
Chemical–Disease Category Linkage (CDCL): Computational Methods Linking Traditional Chinese Medicines and Western Therapeutics
Johnson, D. E., & Cheung, K. W. K. (2017) · Royal Society of Chemistry eBooks · doi:10.1039/9781782623731-00279
Interrogating Drug Mechanism of Action Using Network Dysregulation Analysis
Jung-Hoon, W. (2015) · doi:10.7916/d8qf8rr1
“The direct targets for all compounds were obtained from the Drugbank database [30] and MATADOR database [31] as well as literature search.”
Method
Bioinformatics for Diseases Management: A Personalized Therapeutics Prospective
Kanhaiya, K. (2017) · Springer eBooks · doi:10.1007/978-3-319-63633-7_11 · Cited by 1
Statistical Machine Learning for Agriculture and Human Health Care Based on Biomedical Big Data
Yamanishi, Y., Tabei, Y., & Kotera, M. (2018) · Mathematics for industry · doi:10.1007/978-981-10-7811-8_12
Bio-relation Discovery and Sparse Learning
Shi, Y. Y. (2012) · ERA: Education and Research Archive (University of Alberta) · doi:10.7939/r3893k
An Efficient Computational Method to Predict Drug-Target Interactions Utilizing Structural Perturbation Method
Lu, X., Liu, F., Li, D., Wang, X., Li, J., & Yuan, Y. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-60802-6_19
Matrix completion techniques in bioinformatics
Mongia, A., & Majumdar, A. (2020) · INFLIBNET
Drug-protein Interaction Prediction based on Improved AdaBoost Algorithm
Gu, W., Xie, X., Zhang, Z., Mao, Y., Liang, Z., & He, Y. (2020) · IEEE International Conference on Automation, Electronics and Electrical Engineering (AUTEEE) · doi:10.1109/auteee50969.2020.9315654
“We used two datasets: (1) MATADOR 1 [19].”
Method
Multi-Target Drugs as Master Keys to Complex Diseases: Inverse Docking Strategies and Opportunities
Méndez, P. S. (2021) · Elsevier eBooks · doi:10.1016/b978-0-12-822312-3.00005-9
CNNEMS: Using Convolutional Neural Networks to Predict Drug-Target Interactions by Combining Protein Evolution and Molecular Structures Information
Yan, X., You, Z., Wang, L., & Chen, P.-P. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-84532-2_51
Cross-platform Screening for Novel Fluvastatin and Kinase Inhibitor Co-therapies in Cancer Cell Lines
Pong, J. T. (2014) · TSpace (University of Toronto)
The assessment of similarity vectors of fingerprint and UMLS in adverse drug reaction predictionPreprint
Besharatifard, M., Ghorbanali, Z., & Zare‐Mirakabad, F. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.11.15.468509
“The directly interacted proteins with drugs of the Mizutani database are extracted from DrugBank[29] and Matador[30] databases.”
Method
HIV Sero Conversion in Urban Rwandan Women Before and After a Prevention Programme
Allen, S. A., Serufilira, A., de Perre, P. V., Lindan, C., Tice, J., Hulley, S. B., & Black, D. M. (1990)
Faculty Opinions recommendation of STITCH: interaction networks of chemicals and proteins.
Bader, G. D., & Wallace, I. M. (2010) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.6754956.7110059
Faculty Opinions recommendation of Generating genome-scale candidate gene lists for pharmacogenomics.
Ramanathan, M. K., & Knights, J. (2009) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.1260972.731058
Does adding the drug-drug similarity to drug-target interaction prediction methods make a noticeable improvement in their efficiency?Preprint
Hassanzadeh, R., & Shabani-Mashcool, S. (2022) · Research Square · doi:10.21203/rs.3.rs-1533175/v1
DTIRF: Predicting Drug-Target Interactions Based on Improved Rotation Forest from Drug Molecular Structure and Protein SequencePreprint
Wang, L., You, Z.-H., Li, L., Yan, X., Zhang, W., & Wang, H. (2019) · doi:10.21203/rs.2.15799/v1
“18 19 20 Drug ID Drug name Taregt protein ID Target protein name Validation source D00691 Dihydroxypropyltheophylline hsa5150 PDE7A_HUMAN SuperTarget D00348 Isotretinoino hsa6256 RXRA_HUMAN SuperTarget D00139 Xanthotoxine hsa1543 CP1A1_HUMAN SuperTarget D02340 Loxapinsuccinate hsa1812 DRD1_HUMAN SuperTarget D00493 Pro…”
MethodInfluential
Systems Toxicology Approaches
Jeong, H., Rho, S., & Hwang, D. (2013) · CRC Press eBooks · doi:10.1201/b15476-10
Prediction of Compound-protein Interactions with Machine Learning Methods
Yamanishi, Y., & Kashima, H. (2011) · IGI Global eBooks · doi:10.4018/978-1-60960-818-7.ch315
Does adding the drug-drug similarity to drug-target interaction prediction methods make a noticeable improvement in their efficiency?Preprint
Hassanzadeh, R., & Shabani-Mashcool, S. (2022) · Research Square · doi:10.21203/rs.3.rs-1533175/v2
Rational computational approaches to predict novel drug candidates against leishmaniasis
Bustamante, C., Muskus, C., & Ochoa, R. (2022) · Annual reports in medicinal chemistry · doi:10.1016/bs.armc.2022.08.005
Drug Repositioning in Response to COVID-19 and other Challenging Diseases
Fomnya, H. J., Ngulde, S. I., Gana, S. M., Bilbonga, G., Amshi, K. A., Midala, C. A., & Garba, K. A. (2022) · American Journal of Pharmacology and Toxicology · doi:10.3844/ajptsp.2022.37.47

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: Kuhn, M.
Dunkel, M.
Campillos, M.
Petsalaki, E.
Jensen, L. J.
Russell, R. B.
Bourne, P. E.
Preissner, R.
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
DOI: 10.1093/nar/gkm862
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: cc-by-nc
creativecommons.org/licenses/by-nc/2.0/uk/ (vor, 3249 d)
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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SuperTarget and Matador: resources for exploring drug-target relationships

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