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

Masaki Asada, Makoto Miwa, Yutaka Sasaki


Abstract
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.
Anthology ID:
P18-2108
Volume:
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
July
Year:
2018
Address:
Melbourne, Australia
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
680–685
Language:
URL:
https://aclanthology.org/P18-2108
DOI:
10.18653/v1/P18-2108
Bibkey:
Cite (ACL):
Masaki Asada, Makoto Miwa, and Yutaka Sasaki. 2018. Enhancing Drug-Drug Interaction Extraction from Texts by Molecular Structure Information. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 680–685, Melbourne, Australia. Association for Computational Linguistics.
Cite (Informal):
Enhancing Drug-Drug Interaction Extraction from Texts by Molecular Structure Information (Asada et al., ACL 2018)
Copy Citation:
PDF:
https://aclanthology.org/P18-2108.pdf
Note:
 P18-2108.Notes.pdf
Presentation:
 P18-2108.Presentation.pdf
Video:
 https://vimeo.com/285805955
Data
DDI