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Accurately predicting drug-drug interactions (DDI) for emerging drugs, whichoffer possibilities for treating and alleviating diseases, with computationalmethods can improve patient care and contribute to efficient drug development.However, many existing computational methods require large amounts of known DDIinformation, which is scarce for emerging drugs. In this paper, we proposeEmerGNN, a graph neural network (GNN) that can effectively predict interactionsfor emerging drugs by leveraging the rich information in biomedical networks.EmerGNN learns pairwise representations of drugs by extracting the pathsbetween drug pairs, propagating information from one drug to the other, andincorporating the relevant biomedical concepts on the paths. The differentedges on the biomedical network are weighted to indicate the relevance for thetarget DDI prediction. Overall, EmerGNN has higher accuracy than existingapproaches in predicting interactions for emerging drugs and can identify themost relevant information on the biomedical network.

Emerging Drug Interaction Prediction Enabled by Flow-based Graph Neural Network with Biomedical Network