September 10, 2025

"Drug Repurposing Based On Heterogenous Networks: A Multi-Modal Graph Based Approach" (Preprint)

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Computational model identifies potential drug candidates for repurposing, suggesting improved clinical decision-making.

The model successfully predicts drug-target interactions in over 1500 FDA-approved drugs, enhancing clinical outcomes.
Ablation experiments reveal that removing modal representation learning significantly reduces model performance across all key metrics.
Multi-modal graph learning framework constructed a large-scale heterogeneous network, demonstrating superior effectiveness over traditional methods.
Potential uses of Macitentan for multiple indications were validated, indicating strong support for drug repurposing in Alzheimer's disease.

Nan et al. (2025) studied this question.

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