DrugMechDB: A Curated Database of Drug Mechanisms

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 evidenc...

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Bibliographic Details
Published in:Scientific data 2023-09, Vol.10 (1), p.632-632, Article 632
Main Authors: Gonzalez-Cavazos, Adriana Carolina, Tanska, Anna, Mayers, Michael, Carvalho-Silva, Denise, Sridharan, Brindha, Rewers, Patrick A., Sankarlal, Umasri, Jagannathan, Lakshmanan, Su, Andrew I.
Format: Article
Language:English
Subjects:
631/114/129
631/114/2401
Accuracy
Computer applications
Data Descriptor
Data models
Disease
Dopamine
Drug development
Drugs
Genomics
Graphs
Humanities and Social Sciences
Interoperability
Knowledge representation
multidisciplinary
Science
Science (multidisciplinary)
Semantics
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Summary: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.
ISSN:2052-4463
2052-4463
DOI:10.1038/s41597-023-02534-z