[Drug repositioning prediction based on dynamic feature learning on heterogeneous graphs]

To address the challenges faced by existing artificial intelligence methods in modeling complex heterogeneous biological networks, particularly their limitations in capturing collaborative relationships between nodes and in extracting high-order topological semantic features, we propose a novel drug repositioning prediction method based on dynamic representation learning on heterogeneous graphs.

A heterogeneous biological graph that integrates drugs, diseases, and their interaction relationships was constructed, based on which a dynamic gated attention module was designed to extract discriminative topological features of drugs and diseases by incorporating a dynamic graph attention mechanism. A gated residual feature fusion mechanism was developed to precisely integrate structural and semantic information from multiple similarity networks to reduce feature redundancy and information loss, thereby enabling accurate prediction of drug-disease associations.

Experiments and case studies conducted on multiple drug datasets related to complex diseases demonstrated that the proposed method outperformed existing mainstream models in drug repositioning prediction.

The proposed method can effectively model complex associations in heterogeneous biological networks, enhance the accuracy of drug repositioning prediction, and provide important technical support for precision treatment of complex diseases and development of medical artificial intelligence.