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.

翻译：准确预测新兴药物（其具有治疗和缓解疾病的可能性）的药物-药物相互作用（DDI），通过计算方法能够改善患者护理并促进高效药物开发。然而，现有计算方法大多需要大量已知的DDI信息，而新兴药物的此类信息极为匮乏。本文提出EmerGNN——一种图神经网络（GNN），通过利用生物医学网络中的丰富信息，可有效预测新兴药物的相互作用。EmerGNN通过提取药物对之间的路径、沿路径从一种药物向另一种药物传播信息，并整合路径上相关的生物医学概念，学习药物对的成对表示。生物医学网络上的不同边被赋予权重，以指示其对目标DDI预测的相关性。总体而言，EmerGNN在预测新兴药物相互作用方面优于现有方法，且能够识别生物医学网络中最相关的信息。