{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,11]],"date-time":"2026-08-11T10:06:12Z","timestamp":1786442772641,"version":"3.56.0"},"reference-count":21,"publisher":"Oxford University Press (OUP)","issue":"12","license":[{"start":{"date-parts":[[2020,10,24]],"date-time":"2020-10-24T00:00:00Z","timestamp":1603497600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["17K12741"],"award-info":[{"award-number":["17K12741"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["20K11962"],"award-info":[{"award-number":["20K11962"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021,7,19]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Motivation<\/jats:title>\n                  <jats:p>Neural methods to extract drug\u2013drug 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.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>We evaluated our approach on the DDIExtraction 2013 shared task dataset. 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 is essential for the improvement.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Availability and implementation<\/jats:title>\n                  <jats:p>Our code is available at https:\/\/github.com\/tticoin\/DESC_MOL-DDIE.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/bioinformatics\/btaa907","type":"journal-article","created":{"date-parts":[[2020,10,9]],"date-time":"2020-10-09T11:19:06Z","timestamp":1602242346000},"page":"1739-1746","source":"Crossref","is-referenced-by-count":80,"title":["Using drug descriptions and molecular structures for drug\u2013drug interaction extraction from literature"],"prefix":"10.1093","volume":"37","author":[{"given":"Masaki","family":"Asada","sequence":"first","affiliation":[{"name":"Toyota Technological Institute, 2-12-1 Hisakata , Tempaku-ku, Nagoya 468-8511, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2330-6972","authenticated-orcid":false,"given":"Makoto","family":"Miwa","sequence":"additional","affiliation":[{"name":"Toyota Technological Institute, 2-12-1 Hisakata , Tempaku-ku, Nagoya 468-8511, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yutaka","family":"Sasaki","sequence":"additional","affiliation":[{"name":"Toyota Technological Institute, 2-12-1 Hisakata , Tempaku-ku, Nagoya 468-8511, Japan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2020,10,24]]},"reference":[{"key":"2023051709461018400_btaa907-B1","first-page":"84","volume-title":"Proceedings of NAACL-HLT 2018","author":"Ammar","year":"2018"},{"key":"2023051709461018400_btaa907-B2","first-page":"680","volume-title":"Proceedings of ACL 2018","author":"Asada","year":"2018"},{"key":"2023051709461018400_btaa907-B3","first-page":"3615","volume-title":"Proceedings of EMNLP-IJCNLP 2019","author":"Beltagy","year":"2019"},{"key":"2023051709461018400_btaa907-B4","first-page":"4171","volume-title":"Proceedings of NAACL-HLT 2019","author":"Devlin","year":"2019"},{"key":"2023051709461018400_btaa907-B5","article-title":"Convolutional networks on graphs for learning molecular fingerprints","year":"2015","journal-title":"Proceedings of NIPS 2015"},{"key":"2023051709461018400_btaa907-B6","article-title":"Gaussian error linear units (GELUs)","author":"Hendrycks","year":"2016","journal-title":"arXiv Preprint arXiv: 1606.08415"},{"key":"2023051709461018400_btaa907-B7","first-page":"66","volume-title":"Proceedings of EMNLP 2018","author":"Kudo","year":"2018"},{"key":"2023051709461018400_btaa907-B8","author":"Landrum","year":"2020"},{"key":"2023051709461018400_btaa907-B9","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1038\/s41928-018-0054-8","article-title":"Mixed-precision in-memory computing","volume":"1","author":"Le Gallo","year":"2018","journal-title":"Nat. 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