Multi-grained Line Graph Neural Network with Hierarchical Contrastive Learning for Predicting Drug-disease Associations

Predicting drug-disease associations is a crucial step in drug repositioning, especially with computational methods that quickly locate potential drug-disease pairs. Heterogenous network is a common tool for introducing multiple type relation information about drugs and diseases. However, the diversity of relations is ignored in most of existing methods, which makes them difficult to explore type semantic information with structure properties. Therefore, we propose a relation-centric GNN framework to encode critical association patterns. Firstly, we utilize a relation-centric graph, line graph, to represent the context of a drug-disease pair identified as the center node. The prediction problem is modeled to learn the embedding vector of the center node. Secondly, a multi-grained line graph neural network (MGLGNN) is designed to excavate fine-grained features that encapsulate local graph structures. We theoretically define a handful of typical nodes that can be regarded as high-order abstractions of relations in each type. Then, MGLGNN distills the local information and passes it to typical nodes from a global perspective. With learned multi-grained features, the center node automatically captures heterogenous relation semantics and structure patterns. Thirdly, a hierarchical contrastive learning (HCL) mechanism is proposed to ensure the quality of multi-grained features in an unsupervised way. Extensive experiments show the great potential of our model in mining drug-disease associations.