DrugMechDB: A Curated Database of Drug Mechanisms.
Journal
Scientific data
ISSN: 2052-4463
Titre abrégé: Sci Data
Pays: England
ID NLM: 101640192
Informations de publication
Date de publication:
16 09 2023
16 09 2023
Historique:
received:
02
05
2023
accepted:
01
09
2023
medline:
18
9
2023
pubmed:
17
9
2023
entrez:
16
9
2023
Statut:
epublish
Résumé
Computational drug repositioning methods have emerged as an attractive and effective solution to find new candidates for existing therapies, reducing the time and cost of drug development. Repositioning methods based on biomedical knowledge graphs typically offer useful supporting biological evidence. This evidence is based on reasoning chains or subgraphs that connect a drug to a disease prediction. However, there are no databases of drug mechanisms that can be used to train and evaluate such methods. Here, we introduce the Drug Mechanism Database (DrugMechDB), a manually curated database that describes drug mechanisms as paths through a knowledge graph. DrugMechDB integrates a diverse range of authoritative free-text resources to describe 4,583 drug indications with 32,249 relationships, representing 14 major biological scales. DrugMechDB can be employed as a benchmark dataset for assessing computational drug repositioning models or as a valuable resource for training such models.
Identifiants
pubmed: 37717042
doi: 10.1038/s41597-023-02534-z
pii: 10.1038/s41597-023-02534-z
pmc: PMC10505144
doi:
Types de publication
Dataset
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
632Subventions
Organisme : NCATS NIH HHS
ID : OT2 TR003427
Pays : United States
Organisme : NCATS NIH HHS
ID : UL1 TR002550
Pays : United States
Organisme : NIA NIH HHS
ID : R01 AG066750
Pays : United States
Commentaires et corrections
Type : UpdateOf
Informations de copyright
© 2023. Springer Nature Limited.
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