A Graph Attention Network-Based Spatial Decomposition Method for Drug Repositioning

Computational drug repositioning technology can identify potential uses for existing drugs and reduce the time and cost required in the drug development process. How to find appropriate representations of drugs and diseases to predict the associations between the two is the main objective of such tasks. With the emergence of graph neural networks in recent years, researchers learned drugs and diseases via graphs in a bid to improve the prediction accuracy. However, there are three key problems that have not been adequately studied: 1) They usually place drug-disease association graphs, drug-drug similarity graphs, and disease-disease similarity graphs under the same semantic space for learning, which lose the higher-order features of the different graphs. 2) They assign equal weight to each neighbor node when aggregating based on the drug-disease association graph, but the effect and mechanism of a drug in treating different diseases are not consistent. 3) They adopt residual connections to enhance the role of the root node without considering that this operation amplifies the effect of anomalous features. In view of this, we first propose a graph attention network-based spatial decomposition method for drug repositioning. Specifically, we reduce the dimensions of the feature space by spatial decomposition and initialize the drug and disease embedding in the drug-similarity subspace, disease-similarity subspace, and drug-disease association subspace, respectively. The representations of drugs and diseases are jointly captured in the corresponding subspace based on similarity and therapeutic associations. Moreover, the extent of the associations is measured through the graph attention mechanism to explore higher-order neighborhood relationships between drugs and diseases. Finally, we introduce a targeted residual connection for personalized propagation of node features. Experiments on four benchmark datasets show that our proposed architecture outperforms current state-of-the-art approaches.