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        <title>Enhancing Drug-Drug Interaction Extraction from Texts by Molecular Structure Information</title>
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    <name type="personal">
        <namePart type="given">Masaki</namePart>
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        <namePart type="given">Makoto</namePart>
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    <name type="personal">
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        <name type="personal">
            <namePart type="given">Iryna</namePart>
            <namePart type="family">Gurevych</namePart>
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        <name type="personal">
            <namePart type="given">Yusuke</namePart>
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    <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.</abstract>
    <identifier type="citekey">asada-etal-2018-enhancing</identifier>
    <identifier type="doi">10.18653/v1/P18-2108</identifier>
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%0 Conference Proceedings
%T Enhancing Drug-Drug Interaction Extraction from Texts by Molecular Structure Information
%A Asada, Masaki
%A Miwa, Makoto
%A Sasaki, Yutaka
%Y Gurevych, Iryna
%Y Miyao, Yusuke
%S Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2018
%8 July
%I Association for Computational Linguistics
%C Melbourne, Australia
%F asada-etal-2018-enhancing
%X 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.
%R 10.18653/v1/P18-2108
%U https://aclanthology.org/P18-2108
%U https://doi.org/10.18653/v1/P18-2108
%P 680-685

Markdown (Informal)

[Enhancing Drug-Drug Interaction Extraction from Texts by Molecular Structure Information](https://aclanthology.org/P18-2108) (Asada et al., ACL 2018)

• Enhancing Drug-Drug Interaction Extraction from Texts by Molecular Structure Information (Asada et al., ACL 2018)

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.