{"id":"https://openalex.org/W2963078194","doi":"https://doi.org/10.18653/v1/p18-2108","title":"Enhancing Drug-Drug Interaction Extraction from Texts by Molecular Structure Information","display_name":"Enhancing Drug-Drug Interaction Extraction from Texts by Molecular Structure Information","publication_year":2018,"publication_date":"2018-01-01","ids":{"openalex":"https://openalex.org/W2963078194","doi":"https://doi.org/10.18653/v1/p18-2108","mag":"2963078194"},"language":"en","primary_location":{"id":"doi:10.18653/v1/p18-2108","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p18-2108","pdf_url":"https://www.aclweb.org/anthology/P18-2108.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.aclweb.org/anthology/P18-2108.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5045914141","display_name":"Masaki Asada","orcid":"https://orcid.org/0000-0002-7117-5962"},"institutions":[{"id":"https://openalex.org/I4840577","display_name":"Toyota Technological Institute","ror":"https://ror.org/001hv0k59","country_code":"JP","type":"education","lineage":["https://openalex.org/I4840577"]}],"countries":["JP"],"is_corresponding":true,"raw_author_name":"Masaki Asada","raw_affiliation_strings":["Computational Intelligence Laboratory Toyota Technological Institute 2-12-1 Hisakata, Tempaku-ku, Nagoya, Aichi, 468-8511, Japan"],"affiliations":[{"raw_affiliation_string":"Computational Intelligence Laboratory Toyota Technological Institute 2-12-1 Hisakata, Tempaku-ku, Nagoya, Aichi, 468-8511, Japan","institution_ids":["https://openalex.org/I4840577"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101412540","display_name":"Makoto Miwa","orcid":"https://orcid.org/0000-0002-2330-6972"},"institutions":[{"id":"https://openalex.org/I4840577","display_name":"Toyota Technological Institute","ror":"https://ror.org/001hv0k59","country_code":"JP","type":"education","lineage":["https://openalex.org/I4840577"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Makoto Miwa","raw_affiliation_strings":["Computational Intelligence Laboratory Toyota Technological Institute 2-12-1 Hisakata, Tempaku-ku, Nagoya, Aichi, 468-8511, Japan"],"affiliations":[{"raw_affiliation_string":"Computational Intelligence Laboratory Toyota Technological Institute 2-12-1 Hisakata, Tempaku-ku, Nagoya, Aichi, 468-8511, Japan","institution_ids":["https://openalex.org/I4840577"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5064752144","display_name":"Yutaka Sasaki","orcid":null},"institutions":[{"id":"https://openalex.org/I4840577","display_name":"Toyota Technological Institute","ror":"https://ror.org/001hv0k59","country_code":"JP","type":"education","lineage":["https://openalex.org/I4840577"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yutaka Sasaki","raw_affiliation_strings":["Computational Intelligence Laboratory Toyota Technological Institute 2-12-1 Hisakata, Tempaku-ku, Nagoya, Aichi, 468-8511, Japan"],"affiliations":[{"raw_affiliation_string":"Computational Intelligence Laboratory Toyota Technological Institute 2-12-1 Hisakata, Tempaku-ku, Nagoya, Aichi, 468-8511, Japan","institution_ids":["https://openalex.org/I4840577"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5045914141"],"corresponding_institution_ids":["https://openalex.org/I4840577"],"apc_list":null,"apc_paid":null,"fwci":2.048,"has_fulltext":true,"cited_by_count":67,"citation_normalized_percentile":{"value":0.87829869,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"680","last_page":"685"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.9988999962806702,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.9988999962806702,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10211","display_name":"Computational Drug Discovery Methods","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9934999942779541,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.727723240852356},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6521183252334595},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.6241499185562134},{"id":"https://openalex.org/keywords/drug","display_name":"Drug","score":0.5735713243484497},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4911670386791229},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4785535931587219},{"id":"https://openalex.org/keywords/information-extraction","display_name":"Information extraction","score":0.4663592576980591},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.4563503861427307},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4366436302661896},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.42559802532196045},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3295897841453552},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.20024776458740234},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.1564655303955078},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.10075852274894714},{"id":"https://openalex.org/keywords/pharmacology","display_name":"Pharmacology","score":0.07954812049865723},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.05963596701622009}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.727723240852356},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6521183252334595},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.6241499185562134},{"id":"https://openalex.org/C2780035454","wikidata":"https://www.wikidata.org/wiki/Q8386","display_name":"Drug","level":2,"score":0.5735713243484497},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4911670386791229},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4785535931587219},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.4663592576980591},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.4563503861427307},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4366436302661896},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.42559802532196045},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3295897841453552},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.20024776458740234},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.1564655303955078},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.10075852274894714},{"id":"https://openalex.org/C98274493","wikidata":"https://www.wikidata.org/wiki/Q128406","display_name":"Pharmacology","level":1,"score":0.07954812049865723},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.05963596701622009},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/p18-2108","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p18-2108","pdf_url":"https://www.aclweb.org/anthology/P18-2108.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/p18-2108","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/p18-2108","pdf_url":"https://www.aclweb.org/anthology/P18-2108.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.4300000071525574,"display_name":"Quality Education"}],"awards":[{"id":"https://openalex.org/G2893431080","display_name":null,"funder_award_id":"17K12741","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"},{"id":"https://openalex.org/G5786340949","display_name":null,"funder_award_id":"KAKENHI Grant Number","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"}],"funders":[{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2963078194.pdf","grobid_xml":"https://content.openalex.org/works/W2963078194.grobid-xml"},"referenced_works_count":25,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1975147762","https://openalex.org/W2018518196","https://openalex.org/W2067704478","https://openalex.org/W2116759194","https://openalex.org/W2148488766","https://openalex.org/W2153579005","https://openalex.org/W2162931300","https://openalex.org/W2170189740","https://openalex.org/W2227583040","https://openalex.org/W2250521169","https://openalex.org/W2264517602","https://openalex.org/W2290847742","https://openalex.org/W2307935645","https://openalex.org/W2584239330","https://openalex.org/W2589489260","https://openalex.org/W2606780347","https://openalex.org/W2611162052","https://openalex.org/W2759136286","https://openalex.org/W2761525601","https://openalex.org/W2785001576","https://openalex.org/W2950898568","https://openalex.org/W2964113829","https://openalex.org/W2964121744","https://openalex.org/W4294170691"],"related_works":["https://openalex.org/W2468279273","https://openalex.org/W2354198838","https://openalex.org/W1989130879","https://openalex.org/W2103419012","https://openalex.org/W2988126442","https://openalex.org/W1974414866","https://openalex.org/W2063982682","https://openalex.org/W2338543196","https://openalex.org/W1544691147","https://openalex.org/W2039176012"],"abstract_inverted_index":{"We":[0,19],"propose":[1],"a":[2],"novel":[3],"neural":[4,26],"method":[5],"to":[6],"extract":[7],"drug-drug":[8],"interactions":[9],"(DDIs)":[10],"from":[11,57],"texts":[12],"using":[13],"external":[14],"drug":[15,22],"molecular":[16,30,59,68],"structure":[17],"information.":[18],"encode":[20],"textual":[21],"pairs":[23,31],"with":[24,32],"convolutional":[25,34],"networks":[27,35],"and":[28,37,66],"their":[29],"graph":[33],"(GCNs),":[36],"then":[38],"we":[39,50],"concatenate":[40],"the":[41,48,58,67,80,83],"outputs":[42],"of":[43,61],"these":[44],"two":[45],"networks.":[46],"In":[47],"experiments,":[49],"show":[51],"that":[52],"GCNs":[53],"can":[54,70],"predict":[55],"DDIs":[56],"structures":[60],"drugs":[62],"in":[63,79],"high":[64],"accuracy":[65],"information":[69],"enhance":[71],"text-based":[72],"DDI":[73],"extraction":[74],"by":[75],"2.39":[76],"percent":[77],"points":[78],"F-score":[81],"on":[82],"DDIExtraction":[84],"2013":[85],"shared":[86],"task":[87],"data":[88],"set.":[89]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":13},{"year":2024,"cited_by_count":11},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":11},{"year":2021,"cited_by_count":10},{"year":2020,"cited_by_count":7},{"year":2019,"cited_by_count":5},{"year":2013,"cited_by_count":1}],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2025-10-10T00:00:00"}