Using Drug Descriptions and Molecular Structures for Drug-Drug Interaction Extraction from Literature

24 Oct 2020  Â·  Masaki Asada, Makoto Miwa, Yutaka Sasaki ·

Motivation Neural methods to extract drug-drug interactions (DDIs) from literature require a large number of annotations. In this study, we propose a novel method to effectively utilize external drug database information as well as information from large-scale plain text for DDI extraction. Specifically, we focus on drug description and molecular structure information as the drug database information. Results We evaluated our approach on the DDIExtraction 2013 shared task data set. We obtained the following results. First, large-scale raw text information can greatly improve the performance of extracting DDIs when combined with the existing model and it shows the state-of-the-art performance. Second, each of drug description and molecular structure information is helpful to further improve the DDI performance for some specific DDI types. Finally, the simultaneous use of the drug description and molecular structure information can significantly improve the performance on all the DDI types. We showed that the plain text, the drug description information, and molecular structure information are complementary and their effective combination are essential for the improvement.

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Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Drug–drug Interaction Extraction DDI extraction 2013 corpus DESC+MOL+SciBERT F1 0.8408 # 1
Micro F1 84.08 # 1


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