Enhancing Drug-Drug Interaction Extraction from Texts by Molecular Structure Information

ACL 2018  ·  Masaki Asada, Makoto Miwa, Yutaka Sasaki ·

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.

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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Drug–drug Interaction Extraction DDI extraction 2013 corpus MOL+CNN F1 0.7255 # 4
Micro F1 72.55 # 5