GCN-BMP: Investigating graph representation learning for DDI prediction task.
Journal:
Methods (San Diego, Calif.)
Published Date:
Jul 3, 2020
Abstract
One drug's pharmacological activity 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 with Bond-aware Message Propagation) to conduct an accurate prediction for DDIs. Our experiments on two real-world datasets demonstrate that GCN-BMP can achieve higher performance 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 that conform to domain knowledge with certain interpretability.