GNDD: A Graph Neural Network-Based Method for Drug-Disease Association Prediction

Potential drug-disease association prediction is important to facilitate drug discovery. However, most of existing drug-disease association prediction approaches rely on assembling multiple drug (disease)-related biological information, which is usually not comprehensively available, and they always fail to explore the latent information in drug-disease network. To tackle these challenges, we propose a graph neural network-based method for drug-disease association prediction, dubbed GNDD, with capturing the complex information between drugs and diseases dispense with any side information. Specifically, GNDD introduces the idea of collaborative filtering in recommendation system to avoid the dependency on multi-data. Furthermore, an embedding propagation strategy is exploited to model the high-order relationships in drug-disease network. We conduct experiments on the Comparative Toxicogenomics Database, demonstrating the effectiveness of our method in drug-disease association prediction.