SuperTarget and Matador: resources for exploring drug-target relationships

By S. Günther, Michael Kuhn, Mathias Dunkel, Monica Campillos, Christian Senger, E. Petsalaki, Jessica Ahmed, Eduardo Garcia Urdiales, A. Gewiess, Lars Juhl Jensen and 6 more. Nucleic Acids Res., 2007

608 citations recorded by the source corpus.

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

Subfields: Computational Theory and Mathematics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/14e05162e86aea806fb97f29be0ab5b2b70bd71d

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Citations in the corpus

Neighborhood Regularized Logistic Matrix Factorization for Drug-Target Interaction Prediction (2016, methodology): “In addition, the practical prediction ability of NRLMF was also confirmed by mapping with the latest version of online biological databases, including ChEMBL [2], DrugBank [30], KEGG [4], and Matador [5].”
A comprehensive map of molecular drug targets (2016, background): “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.”
BioTriangle: a web-accessible platform for generating various molecular representations for chemicals, proteins, DNAs/RNAs and their interactions (2016, background): “Consequently, public databases for human-specific molecular interaction data have been undergoing a rapid growth within the past decade, such as BIND [18], DIP [19], STITCH [20], HPRD [21], TTD [22], DrugBank [23], ChEMBL [24], KEGG [25], BindingDB [26], SuperTarget and Matador [27], to name a few.”
Similarity-based machine learning methods for predicting drug-target interactions: a brief review (2014, methodology): “Data on drugs, targets and drug–target interactions are available in the following databases: KEGG BRITE [9], BRENDA [39], SuperTarget [40], DrugBank [41], DCDB [42] and ChEBI [43].”
STITCH: interaction networks of chemicals and proteins (2007, background): “To assess the relevance of the association by a shared pharmacological action, the number of chemicals annotated with each pharmacological action is determined and benchmarked against the probability of sharing a drug target in the MATADOR database.”
Structure-based Systems Biology for Analyzing Off-target Binding (2011, background): “As shown in Figure 2, among 6,127 drugs or drug candidates in the SuperTarget database [27], around 15% and 60% of them have molecular fingerprint similarity scores larger than 90 and 70, respectively, to ligands deposited in PDB. Comprehensive coverage by structures and ligands provides an…”