Emerging Drug Interaction Prediction Enabled by Flow-based Graph Neural Network with Biomedical Network
This work addresses a critical bottleneck in drug development and patient care by enabling more accurate predictions for new drugs, though it is an incremental improvement over existing computational approaches.
The paper tackles the problem of predicting drug-drug interactions for emerging drugs, which lack known interaction data, by proposing EmerGNN, a graph neural network that leverages biomedical networks to achieve higher accuracy than existing methods.
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.