The source provides excerpts from a scientific paper introducing **TxGNN**, a novel graph foundation model designed for **zero-shot drug repurposing**, which aims to identify therapeutic candidates even for diseases with no existing treatments or limited molecular data. Developed by researchers affiliated with institutions like Harvard Medical School and Stanford University, this model leverages a **medical knowledge graph (KG)** and a graph neural network (GNN) to predict drug indications and contraindications across over 17,000 diseases, demonstrating significant performance improvements over existing methods. The paper highlights TxGNN’s ability to generate **multi-hop interpretable explanations** for its predictions, fostering trust and aiding human experts, and validates its clinical relevance by showing alignment with **off-label prescriptions** observed in electronic medical records (EMRs). Overall, the work presents a comprehensive AI framework to systemize and enhance drug repurposing, particularly for neglected or rare diseases.Source:https://pmc.ncbi.nlm.nih.gov/articles/PMC11645266/

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