  We propose an end-to-end model to predict drug-drug interactions (DDIs) by
employing graph-augmented convolutional networks. And this is implemented by
combining graph CNN with an attentive pooling network to extract structural
relations between drug pairs and make DDI predictions. The experiment results
suggest a desirable performance achieving ROC at 0.988, F1-score at 0.956, and
AUPR at 0.986. Besides, the model can tell how the two DDI drugs interact
structurally by varying colored atoms. And this may be helpful for drug design
during drug discovery.
