Emerging Drug Interaction Prediction Enabled by Flow-based Graph Neural
  Network with Biomedical Network
Yongqi Zhang Quanming Yao Ling Yue Xian Wu Ziheng Zhang Zhenxi Lin Yefeng Zheng
Accurately predicting drug-drug interactions (DDI) for emerging drugs, which
offer possibilities for treating and alleviating diseases, with computational
methods can improve patient care and contribute to efficient drug development.
However, many existing computational methods require large amounts of known DDI
information, which is scarce for emerging drugs. In this paper, we propose
EmerGNN, a graph neural network (GNN) that can effectively predict interactions
for emerging drugs by leveraging the rich information in biomedical networks.
EmerGNN learns pairwise representations of drugs by extracting the paths
between drug pairs, propagating information from one drug to the other, and
incorporating the relevant biomedical concepts on the paths. The different
edges on the biomedical network are weighted to indicate the relevance for the
target DDI prediction. Overall, EmerGNN has higher accuracy than existing
approaches in predicting interactions for emerging drugs and can identify the
most relevant information on the biomedical network.