A foundation model for clinician-centered drug repurposing Article Swipe
Kexin Huang
,
Payal Chandak
,
Qianwen Wang
,
Shreyas Havaldar
,
Akhil Vaid
,
Jure Leskovec
,
Girish N. Nadkarni
,
Benjamin S. Glicksberg
,
Nils Gehlenborg
,
Marinka Žitnik
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1101/2023.03.19.23287458
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1101/2023.03.19.23287458
Drug repurposing – identifying new therapeutic uses for approved drugs – is often serendipitous and opportunistic, expanding the use of drugs for new diseases. The clinical utility of drug repurposing AI models remains limited because the models focus narrowly on diseases for which some drugs already exist. Here, we introduce T x GNN, a graph foundation model for zero-shot drug repurposing, identifying therapeutic candidates even for diseases with limited treatment options or no existing drugs. Trained on a medical knowledge graph, T x GNN utilizes a graph neural network and metric-learning module to rank drugs as potential indications and contraindications across 17,080 diseases. When benchmarked against eight methods, T x GNN improves prediction accuracy for indications by 49.2% and contraindications by 35.1% under stringent zero-shot evaluation. To facilitate model interpretation, T x GNN’s Explainer module offers transparent insights into multi-hop medical knowledge paths that form T x GNN’s predictive rationales. Human evaluation of T x GNN’s Explainer showed that T x GNN’s predictions and explanations perform encouragingly on multiple axes of performance beyond accuracy. Many of T x GNN’s novel predictions align with off-label prescriptions clinicians make in a large healthcare system. T x GNN’s drug repurposing predictions are accurate, consistent with off-label drug use, and can be investigated by human experts through multi-hop interpretable rationales.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2023.03.19.23287458
- https://www.medrxiv.org/content/medrxiv/early/2023/03/20/2023.03.19.23287458.full.pdf
- OA Status
- green
- Cited By
- 14
- References
- 48
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4327909454
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4327909454Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2023.03.19.23287458Digital Object Identifier
- Title
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A foundation model for clinician-centered drug repurposingWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-03-20Full publication date if available
- Authors
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Kexin Huang, Payal Chandak, Qianwen Wang, Shreyas Havaldar, Akhil Vaid, Jure Leskovec, Girish N. Nadkarni, Benjamin S. Glicksberg, Nils Gehlenborg, Marinka ŽitnikList of authors in order
- Landing page
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https://doi.org/10.1101/2023.03.19.23287458Publisher landing page
- PDF URL
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https://www.medrxiv.org/content/medrxiv/early/2023/03/20/2023.03.19.23287458.full.pdfDirect link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
- OA URL
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https://www.medrxiv.org/content/medrxiv/early/2023/03/20/2023.03.19.23287458.full.pdfDirect OA link when available
- Concepts
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Repurposing, Foundation (evidence), Drug repositioning, Drug, Computer science, Medicine, Pharmacology, Engineering, Political science, Law, Waste managementTop concepts (fields/topics) attached by OpenAlex
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14Total citation count in OpenAlex
- Citations by year (recent)
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2025: 8, 2024: 4, 2023: 2Per-year citation counts (last 5 years)
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48Number of works referenced by this work
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-
10Other works algorithmically related by OpenAlex
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