Computational drug repositioning with attention walking.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
02 May 2024
02 May 2024
Historique:
received:
21
04
2023
accepted:
26
04
2024
medline:
3
5
2024
pubmed:
3
5
2024
entrez:
2
5
2024
Statut:
epublish
Résumé
Drug repositioning aims to identify new therapeutic indications for approved medications. Recently, the importance of computational drug repositioning has been highlighted because it can reduce the costs, development time, and risks compared to traditional drug discovery. Most approaches in this area use networks for systematic analysis. Inferring drug-disease associations is then defined as a link prediction problem in a heterogeneous network composed of drugs and diseases. In this article, we present a novel method of computational drug repositioning, named drug repositioning with attention walking (DRAW). DRAW proceeds as follows: first, a subgraph enclosing the target link for prediction is extracted. Second, a graph convolutional network captures the structural features of the labeled nodes in the subgraph. Third, the transition probabilities are computed using attention mechanisms and converted into random walk profiles. Finally, a multi-layer perceptron takes random walk profiles and predicts whether a target link exists. As an experiment, we constructed two heterogeneous networks with drug-drug similarities based on chemical structures and anatomical therapeutic chemical classification (ATC) codes. Using 10-fold cross-validation, DRAW achieved an area under the receiver operating characteristic (ROC) curve of 0.903 and outperformed state-of-the-art methods. Moreover, we demonstrated the results of case studies for selected drugs and diseases to further confirm the capability of DRAW to predict drug-disease associations.
Identifiants
pubmed: 38698208
doi: 10.1038/s41598-024-60756-6
pii: 10.1038/s41598-024-60756-6
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
10072Subventions
Organisme : National Research Foundation of Korea (NRF)Science
ID : 2021R1A2C1011946
Organisme : Ministry of Education (Ministry of Education of the Republic of Korea)
ID : 2022RIS-005
Informations de copyright
© 2024. The Author(s).
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