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A foundation model for clinician-centered drug repurposing. | LitMetric

AI Article Synopsis

  • Drug repurposing means using old, already approved medicines for new diseases.
  • The TXGNN model can help find new uses for drugs, even for diseases that have no treatments yet, and it's way better at predicting drug uses than other methods.
  • TXGNN not only predicts where drugs can be used but also explains its predictions clearly, making it easier for doctors to understand and investigate its suggestions.

Article Abstract

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 TXGNN, 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, TXGNN 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, TXGNN improves prediction accuracy for indications by 49.2% and contraindications by 35.1% under stringent zero-shot evaluation. To facilitate model interpretation, TXGNN's Explainer module offers transparent insights into multi-hop medical knowledge paths that form TXGNN's predictive rationales. Human evaluation of TXGNN's Explainer showed that TXGNN's predictions and explanations perform encouragingly on multiple axes of performance beyond accuracy. Many of TxGNN's novel predictions align with off-label prescriptions clinicians make in a large healthcare system. TXGNN'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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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11326339PMC
http://dx.doi.org/10.1101/2023.03.19.23287458DOI Listing

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