Dual-route embedding-aware graph neural networks for drug repositioning.

ABSTRACT: Drug repositioning presents a compelling strategy to accelerate therapeutic development by uncovering new indications for existing compounds. However, current computational methods are often limited in their ability to integrate heterogeneous biomedical data and model the intricate, multiscale relationships underlying drug-disease associations, while large-scale experimental validation remains prohibitively resource-intensive. Here, we present DREAM-GNN (Dual-Route Embedding-Aware Model for Graph Neural Networks), a multiview deep graph learning framework that incorporates biomedical domain knowledge with two complementary graphs capturing both topological structure and feature similarity to enable accurate and biologically meaningful prediction of drug-disease associations. Extensive expe

SUBMITTER: Zhao Y 

PROVIDER: S-EPMC12554636 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature