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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

2020-Jul-02

DDI, Graph Representation Learning, Interpretability, Robustness, Scalability