DrugBank: a knowledgebase for drugs, drug actions and drug targets

By David S. Wishart, Craig Knox, Anchi Guo, D. Cheng, Savita Shrivastava, D. Tzur, Bijaya Gautam, Murtaza Hassanali. 2007

2,750 citations recorded by the source corpus.

DrugBank is a richly annotated resource that combines detailed drug data with comprehensive drug target and drug action information. Since its first release in 2006, DrugBank has been widely used to facilitate in silico drug target discovery, drug design, drug docking or screening, drug metabolism prediction, drug interaction prediction and general pharmaceutical education. The latest version of DrugBank (release 2.0) has been expanded significantly over the previous release. With ∼4900 drug entries, it now contains 60% more FDA-approved small molecule and biotech drugs including 10% more ‘experimental’ drugs. Significantly, more protein target data has also been added to the database, with the latest version of DrugBank containing three times as many non-redundant protein or drug target sequences as before (1565 versus 524). Each DrugCard entry now contains more than 100 data fields with half of the information being devoted to drug/chemical data and the other half devoted to pharmacological, pharmacogenomic and molecular biological data. A number of new data fields, including food–drug interactions, drug–drug interactions and experimental ADME data have been added in response to numerous user requests. DrugBank has also significantly improved the power and simplicity of its structure query and text query searches. DrugBank is available at http://www.drugbank.ca

Subfields: Computational Theory and Mathematics, Molecular Biology, Pharmacology

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Citations in the corpus

SWEETLEAD: an In Silico Database of Approved Drugs, Regulated Chemicals, and Herbal Isolates for Computer-Aided Drug Discovery (2013, background): “While several databases are available containing vast amounts of chemical data[22-30], when querying these databases for a given drug conflicting information is occasionally retrieved.”
DrugBank 4.0: shedding new light on drug metabolism (2013, background): “0, along with a significant increase in the number of approved and experimental drugs (9).”
Bridging semantics and syntax with graph algorithms - state-of-the-art of extracting biomedical relations (2017, background): “Shared resources for biomedical relation extraction Utility category Data sources Terminology/Ontology/Knowledge Base GO [161], UMLS [58], MeSH [162], Wordnet [118], Verbnet [163], Biothesaurus [122], PDB [101], Uniprot [121], SIDER [164], DrugBank [165], HIVDB [166], RegaDB [167], Entrez Gene [153]”
IUPHAR-DB: new receptors and tools for easy searching and visualization of pharmacological data (2010, methodology): “In addition to text-based searches of the database content the search tools now allow receptor and ligand retrieval by external identifier, such as by protein accession or DrugBank identifier.”
DrugBank 3.0: a comprehensive resource for ‘Omics’ research on drugs (2010, background): “Details relating to DrugBank’s overall design, general querying capabilities, curation protocols, structure depiction conventions, quality assurance and drug selection criteria have been described previously (12,13).”
Systems Pharmacology: Network Analysis to Identify Multiscale Mechanisms of Drug Action (2012, background): “The simplest network has a directed edge connecting a drug node to its target protein node (12).”
BATMAN-TCM: a Bioinformatics Analysis Tool for Molecular mechANism of Traditional Chinese Medicine (2016, methodology): “Here, the GSP dataset was extracted from DrugBank database (version: 20150726) 43 , including 5481 known drug-target interactions between 1204 drugs and 1212 targets, and only those interactions whose drugs were FDA-approved and had InChI structural information 44 , and whose targets were human…”
Open PHACTS: semantic interoperability for drug discovery. (2012, methodology): “These include data from well-known databases such as PubChem [47], DrugBank [48,49] and ChemSpi-der.”
MEROPS: the database of proteolytic enzymes, their substrates and inhibitors (2013, background): “New cross-references have been established between SMIs and the ChEBI (17) and DrugBank (18) databases, with 100 and 40 cross-references, respectively.”
Large-Scale Off-Target Identification Using Fast and Accurate Dual Regularized One-Class Collaborative Filtering and Its Application to Drug Repurposing (2016, methodology): “While publicly available biological and/or chemical databases (e.g. ZINC [18], ChEMBL [19], DrugBank [20], PubChem [21], and UniProt [22]) have enabled large-scale screening of drug-target associations, the known chemical-protein associations are sparse, and the number of reported negative cases…”
Predicting drug-target interactions from chemical and genomic kernels using Bayesian matrix factorization (2012, methodology): “In order to demonstrate the performance of our proposed method, called ‘kernelized Bayesian matrix factorization with twin kernels’ (KBMF2K), we use four benchmark datasets containing known drug–target interaction networks, chemical kernels between drug compounds and genomic kernels between target p”
HIT: linking herbal active ingredients to targets (2010, background): “Crosslinks have been made to TTD, DrugBank, KEGG, PDB, Uniprot, Pfam, NCBI, TCM-ID and other databases.”
Computational drug discovery (2012, methodology): “The target protein structures in complex with small molecules were carefully extracted from the DrugBank [26] , BindingDB [27] , PDBBind [28] , and PDTD [24] databases, and over 7000 pharmacophore models (covering information for over 1500 drug npg targets) based on the complex structures were…”
Network pharmacology: towards the artificial intelligence-based precision traditional Chinese medicine (2023, background): “Thus, the network relationships also include those between compounds or natural products and their relevant molecular targets mined from databases such as HIT [36],TCMGeneDIT [37],DrugCentral [38] and DrugBank [39].”
Predicting potential drug-drug interactions by integrating chemical, biological, phenotypic and network data (2017, background): “DrugBank database [10, 11, 16, 17] is a bioinformatics resource with drug targets, drug enzymes and drug transporters.”