A Hypergraph Convolutional Network With Explicit High-Order Interaction Information Extraction for Drug Repositioning

Drug repositioning, a promising strategy in drug development, aims to identify new indications for existing drugs while reducing costs and safety risks. Leveraging their unique advantages in modeling higher-order relations among nodes, hypergraphs and hypergraph neural networks (HGNN) have become increasingly popular in drug repositioning. However, most HGNN-based methods overlook the diverse relations generated during the convolution and do not explicitly model high-order interactions, limiting their ability to capture high-order interaction information adequately. To address these limitations, we propose HGCNDR, a hypergraph convolutional network with explicit high-order interaction extraction for drug repositioning. HGCNDR introduces a relation-aware hypergraph convolution operation to handle distinct relation types and a Hadamard product-based strategy to effectively model high-order interactions among drugs and diseases, efficiently extracting the resulting high-order interaction information. Specifically, HGCNDR constructs two feature graphs and a hypergraph based on drug similarity features, disease similarity features, and drug-disease association networks. HGCNDR then employs graph convolutional networks to extract embeddings from the feature graphs, while using the relation-aware hypergraph convolution operation and the strategy to extract structural and high-order interaction information embeddings from the hypergraph. Additionally, to preserve the common semantics between the embeddings extracted from the feature graphs and the hypergraph, HGCNDR introduces a consistency constraint. The experimental results demonstrate that HGCNDR has competitive performance compared to several baseline methods. Moreover, case studies on Alzheimer's disease and Breast carcinoma confirm that HGCNDR can retrieve more actual drug-disease associations in the top prediction results.