Discover novel drug candidates
with DrugBank data

DrugBank makes data integrations easy by providing extensive synonyms, external identifiers, formulations, salt forms and chemical structures. Our customers use these to facilitate data integration and cross-mapping with other datasets. External mappings include MedDRA, ICD-10, SNOMED-CT, Uniprot, PDB, UNII, CAS, InChI, InChIKey, NDC, NDA, EMA and ATC codes.

Drug discovery customers find our pharmacology dataset useful for understanding the mechanisms of existing drugs, or potential mechanisms of candidates, for building similarity-based predictors, for identifying potential repurposing opportunities, and for building new treatments for diseases. Our pharmacology dataset includes detailed descriptions of the mechanism of action, metabolism, absorption, distribution, elimination and pharmacokinetic and pharmacodynamic parameters such as half-life, clearance, and LD50.

Current customers utilize our robust clinical trial information, including trial date, ID, title, phase, date started/stopped, trial description, conditions (indication), intervention groups, PubMed references for the trial, countries, and intervention descriptions. Information from clinicaltrials.gov has been parsed, normalized and integrated directly with DrugBank datasets.

With over 27,000 drug-protein interactions, covering targets, enzymes, carriers, and transporters, DrugBank’s drug-protein relationships dataset helps our customers create a foundation of authenticated research for drug discovery. Drug-protein relationships cover targets, off-target interactions, enzymes, carriers and transporter. This includes annotations describing the pharmacological action, and the type of interaction (antagonist, agonist, substrate, inhibitor, inducer). The interactions also include the protein and gene identifiers and sequences and are associated with over 17,000 references.

The drug metabolism dataset provides structured descriptions of every step in the metabolism of a drug including the enzymes involved and the chemical structures of every metabolite. This dataset is used by our customers to understand the metabolism of existing drugs, explore potential causes for adverse effects, and in modelling or predicting the metabolism of drug candidates.

The detailed drug and disease pathway dataset can be used to explore downstream effects to identify potential mechanisms, explore the effects of drug combinations, and to enrich experimental results for better insights.