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In Methods (San Diego, Calif.)

The pharmacological activity of one drug may be changed unexpectedly, owing to the concurrent administration of another drug. It is likely to cause unexpected drug-drug interactions (DDIs). Several machine learning approaches have been proposed to predict the occurrence of DDIs. However, existing approaches are almost dependent heavily on various drug-related features, which may incur noisy inductive bias. To alleviate this problem, we investigate the utilization of the end-to-end graph representation learning for the DDI prediction task. We establish a novel DDI prediction method named GCN-BMP (Graph Convolutional Network wth Bond-aware Message Propagation) to conduct the accurate prediction for DDIs. Our experiments on two real-world datasets demonstrate that GCN-BMP can achieve higher performance when compared to various baseline approaches. Moreover, in the light of the self-contained attention mechanism in our GCN-BMP, we could find the most vital local atoms which are conforming to domain knowledge with certain interpretability.

Chen Xin, Liu China Xien, Wu China Ji


DDI, Graph Representation Learning, Interpretability, Robustness, Scalability