We propose a novel neural method to extract drug-drug interactions (DDIs)
from texts using external drug molecular structure information. We encode
textual drug pairs with convolutional neural networks and their molecular pairs
with graph convolutional networks (GCNs), and then we concatenate the outputs
of these two networks. In the experiments, we show that GCNs can predict DDIs
from the molecular structures of drugs in high accuracy and the molecular
information can enhance text-based DDI extraction by 2.39 percent points in the
F-score on the DDIExtraction 2013 shared task data set.