{"id":"https://openalex.org/W3007148168","doi":"https://doi.org/10.1109/bigdata47090.2019.9006087","title":"Learning Relevant Molecular Representations via Self-Attentive Graph Neural Networks","display_name":"Learning Relevant Molecular Representations via Self-Attentive Graph Neural Networks","publication_year":2019,"publication_date":"2019-12-01","ids":{"openalex":"https://openalex.org/W3007148168","doi":"https://doi.org/10.1109/bigdata47090.2019.9006087","mag":"3007148168"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata47090.2019.9006087","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata47090.2019.9006087","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://eprints.lib.hokudai.ac.jp/dspace/bitstream/2115/76909/1/kikuchi-dglma2019.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5062628989","display_name":"Shoma Kikuchi","orcid":null},"institutions":[{"id":"https://openalex.org/I205349734","display_name":"Hokkaido University","ror":"https://ror.org/02e16g702","country_code":"JP","type":"education","lineage":["https://openalex.org/I205349734"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Shoma Kikuchi","raw_affiliation_strings":["Hokkaido University, Sapporo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hokkaido University, Sapporo, Japan","institution_ids":["https://openalex.org/I205349734"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012617043","display_name":"Ichigaku Takigawa","orcid":"https://orcid.org/0000-0001-5633-995X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ichigaku Takigawa","raw_affiliation_strings":["RIKEN, Kyoto, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"RIKEN, Kyoto, Japan","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056709028","display_name":"Satoshi Oyama","orcid":"https://orcid.org/0000-0002-8124-3578"},"institutions":[{"id":"https://openalex.org/I205349734","display_name":"Hokkaido University","ror":"https://ror.org/02e16g702","country_code":"JP","type":"education","lineage":["https://openalex.org/I205349734"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Satoshi Oyama","raw_affiliation_strings":["Hokkaido University, Sapporo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hokkaido University, Sapporo, Japan","institution_ids":["https://openalex.org/I205349734"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054412358","display_name":"Masahito Kurihara","orcid":"https://orcid.org/0000-0002-1478-1093"},"institutions":[{"id":"https://openalex.org/I205349734","display_name":"Hokkaido University","ror":"https://ror.org/02e16g702","country_code":"JP","type":"education","lineage":["https://openalex.org/I205349734"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Masahito Kurihara","raw_affiliation_strings":["Hokkaido University, Sapporo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hokkaido University, Sapporo, Japan","institution_ids":["https://openalex.org/I205349734"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.23095432,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"28","issue":null,"first_page":"5364","last_page":"5369"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10211","display_name":"Computational Drug Discovery Methods","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T10211","display_name":"Computational Drug Discovery Methods","score":0.9998999834060669,"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/T11948","display_name":"Machine Learning in Materials Science","score":0.9994999766349792,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10044","display_name":"Protein Structure and Dynamics","score":0.9513999819755554,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.7398821115493774},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6709233522415161},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5845171213150024},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5585059523582458},{"id":"https://openalex.org/keywords/molecular-graph","display_name":"Molecular graph","score":0.4980430603027344},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.45014527440071106},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.44868624210357666},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.338100790977478},{"id":"https://openalex.org/keywords/chemistry","display_name":"Chemistry","score":0.17308253049850464}],"concepts":[{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.7398821115493774},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6709233522415161},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5845171213150024},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5585059523582458},{"id":"https://openalex.org/C2780022179","wikidata":"https://www.wikidata.org/wiki/Q1986794","display_name":"Molecular graph","level":3,"score":0.4980430603027344},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45014527440071106},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.44868624210357666},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.338100790977478},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.17308253049850464},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/bigdata47090.2019.9006087","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata47090.2019.9006087","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},{"id":"pmh:oai:eprints.lib.hokudai.ac.jp:2115/76909","is_oa":true,"landing_page_url":"http://hdl.handle.net/2115/76909","pdf_url":"https://eprints.lib.hokudai.ac.jp/dspace/bitstream/2115/76909/1/kikuchi-dglma2019.pdf","source":{"id":"https://openalex.org/S4306400549","display_name":"Hokkaido University Collection of Scholarly and Academic Papers (Hokkaido University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205349734","host_organization_name":"Hokkaido University","host_organization_lineage":["https://openalex.org/I205349734"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"proceedings (author version)"},{"id":"pmh:oai:irdb.nii.ac.jp:01364:0007190175","is_oa":true,"landing_page_url":"https://hdl.handle.net/2115/76909","pdf_url":"https://eprints.lib.hokudai.ac.jp/repo/huscap/all/76909/kikuchi-dglma2019.pdf","source":{"id":"https://openalex.org/S7407056385","display_name":"Institutional Repositories DataBase (IRDB)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I184597095","host_organization_name":"National Institute of Informatics","host_organization_lineage":["https://openalex.org/I184597095"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"2019 IEEE International Conference on Big Data (Big Data)","raw_type":"conference paper"}],"best_oa_location":{"id":"pmh:oai:eprints.lib.hokudai.ac.jp:2115/76909","is_oa":true,"landing_page_url":"http://hdl.handle.net/2115/76909","pdf_url":"https://eprints.lib.hokudai.ac.jp/dspace/bitstream/2115/76909/1/kikuchi-dglma2019.pdf","source":{"id":"https://openalex.org/S4306400549","display_name":"Hokkaido University Collection of Scholarly and Academic Papers (Hokkaido University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205349734","host_organization_name":"Hokkaido University","host_organization_lineage":["https://openalex.org/I205349734"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"proceedings (author version)"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G207210898","display_name":null,"funder_award_id":"17K19953","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"},{"id":"https://openalex.org/G2499681461","display_name":null,"funder_award_id":"15H05711","funder_id":"https://openalex.org/F4320338075","funder_display_name":"Core Research for Evolutional Science and Technology"},{"id":"https://openalex.org/G384940901","display_name":null,"funder_award_id":"15H05711","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"},{"id":"https://openalex.org/G4194865766","display_name":null,"funder_award_id":"17H01783","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"},{"id":"https://openalex.org/G6718509927","display_name":null,"funder_award_id":"CREST","funder_id":"https://openalex.org/F4320334764","funder_display_name":"Japan Society for the Promotion of Science"}],"funders":[{"id":"https://openalex.org/F4320323533","display_name":"Hokkaido University","ror":"https://ror.org/02e16g702"},{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"},{"id":"https://openalex.org/F4320336288","display_name":"Global Institution for Collaborative Research and Education, Hokkaido University","ror":null},{"id":"https://openalex.org/F4320338075","display_name":"Core Research for Evolutional Science and Technology","ror":"https://ror.org/00097mb19"},{"id":"https://openalex.org/F4320338111","display_name":"Precursory Research for Embryonic Science and Technology","ror":null}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3007148168.pdf","grobid_xml":"https://content.openalex.org/works/W3007148168.grobid-xml"},"referenced_works_count":9,"referenced_works":["https://openalex.org/W1988037271","https://openalex.org/W2048080607","https://openalex.org/W2076498053","https://openalex.org/W2134164499","https://openalex.org/W2606780347","https://openalex.org/W2964113829","https://openalex.org/W4297733535","https://openalex.org/W6685350579","https://openalex.org/W6736685754"],"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/W2057568687","https://openalex.org/W2063982682","https://openalex.org/W2013842271","https://openalex.org/W2808877228"],"abstract_inverted_index":{"Molecular":[0],"graphs":[1],"are":[2],"one":[3],"of":[4,61,92,98],"the":[5,96,114],"established":[6],"representations":[7],"for":[8,77],"small":[9],"molecules,":[10],"and":[11,22],"even":[12],"steric":[13],"or":[14],"electronic":[15],"information":[16,50,76],"can":[17],"be":[18],"encoded":[19],"as":[20,85],"node":[21],"edge":[23],"features.":[24],"Naturally,":[25],"graph":[26,62,82],"neural":[27,63,81],"networks":[28,64],"have":[29],"been":[30],"intensively":[31],"investigated":[32],"to":[33,46,71,110],"solve":[34],"various":[35],"chemical":[36,49,75],"problems":[37],"at":[38,68],"molecular":[39],"levels.":[40],"However,":[41],"it":[42],"remains":[43],"unclear":[44],"how":[45],"encode":[47],"relevant":[48,74],"into":[51],"graphs.":[52],"We":[53],"investigate":[54],"this":[55],"problem":[56],"by":[57],"proposing":[58],"three":[59,90],"models":[60],"with":[65],"self-attention":[66,107],"mechanisms":[67,94,108],"different":[69],"levels":[70],"adaptively":[72],"select":[73],"each":[78],"input.":[79],"Using":[80],"fingerprint":[83],"(NFP)":[84],"a":[86],"baseline,":[87],"we":[88],"introduce":[89],"types":[91],"attention":[93,124],"on":[95],"top":[97],"NFPs.":[99],"Our":[100],"experimental":[101],"evaluations":[102],"suggest":[103],"that":[104],"introducing":[105],"these":[106],"contributes":[109],"not":[111],"only":[112],"improving":[113],"prediction":[115],"accuracy":[116],"but":[117],"also":[118],"providing":[119],"quantitative":[120],"interpretation":[121],"using":[122],"obtained":[123],"coefficients.":[125]},"counts_by_year":[],"updated_date":"2026-06-17T08:01:34.144755","created_date":"2025-10-10T00:00:00"}
