{"id":"https://openalex.org/W4401857142","doi":"https://doi.org/10.1145/3637528.3672043","title":"Learning Multi-view Molecular Representations with Structured and Unstructured Knowledge","display_name":"Learning Multi-view Molecular Representations with Structured and Unstructured Knowledge","publication_year":2024,"publication_date":"2024-08-24","ids":{"openalex":"https://openalex.org/W4401857142","doi":"https://doi.org/10.1145/3637528.3672043"},"language":"en","primary_location":{"id":"doi:10.1145/3637528.3672043","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3637528.3672043","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3637528.3672043","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 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3637528.3672043","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5060171040","display_name":"Yizhen Luo","orcid":"https://orcid.org/0000-0002-7107-378X"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yizhen Luo","raw_affiliation_strings":["Institute for AI Industry Research (AIR), Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Institute for AI Industry Research (AIR), Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085421548","display_name":"Kai Yang","orcid":"https://orcid.org/0009-0005-4961-2715"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kai Yang","raw_affiliation_strings":["Institute for AI Industry Research (AIR), Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Institute for AI Industry Research (AIR), Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019862582","display_name":"Massimo Hong","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Massimo Hong","raw_affiliation_strings":["Institute for AI Industry Research (AIR), Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Institute for AI Industry Research (AIR), Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115597846","display_name":"Xing Yi Liu","orcid":"https://orcid.org/0000-0001-9452-7695"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xing Yi Liu","raw_affiliation_strings":["Institute for AI Industry Research (AIR), Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Institute for AI Industry Research (AIR), Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5099172406","display_name":"Zikun Nie","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zikun Nie","raw_affiliation_strings":["Institute for AI Industry Research (AIR), Tsinghua University & Pharmolix Inc., Beijing, China","Institute for AI Industry Research (AIR), Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Institute for AI Industry Research (AIR), Tsinghua University & Pharmolix Inc., Beijing, China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Institute for AI Industry Research (AIR), Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102016321","display_name":"Hao Zhou","orcid":"https://orcid.org/0000-0002-1458-1016"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hao Zhou","raw_affiliation_strings":["Institute for AI Industry Research (AIR), Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Institute for AI Industry Research (AIR), Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5047496977","display_name":"Zaiqing Nie","orcid":"https://orcid.org/0000-0002-1134-2343"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zaiqing Nie","raw_affiliation_strings":["Institute for AI Industry Research (AIR), Tsinghua University & Pharmolix Inc., Beijing, China","Institute for AI Industry Research (AIR), Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Institute for AI Industry Research (AIR), Tsinghua University & Pharmolix Inc., Beijing, China","institution_ids":["https://openalex.org/I99065089"]},{"raw_affiliation_string":"Institute for AI Industry Research (AIR), Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5060171040"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":3.2552,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.92918642,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"2082","last_page":"2093"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10211","display_name":"Computational Drug Discovery Methods","score":0.9997000098228455,"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":0.9997000098228455,"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":0.9970999956130981,"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/T12254","display_name":"Machine Learning in Bioinformatics","score":0.9782999753952026,"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/computer-science","display_name":"Computer science","score":0.7095907926559448},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.670056939125061},{"id":"https://openalex.org/keywords/molecular-graph","display_name":"Molecular graph","score":0.5006959438323975},{"id":"https://openalex.org/keywords/comprehension","display_name":"Comprehension","score":0.492591917514801},{"id":"https://openalex.org/keywords/property","display_name":"Property (philosophy)","score":0.41657066345214844},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.407585084438324},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.39118900895118713},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.20589479804039001},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.12608671188354492}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7095907926559448},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.670056939125061},{"id":"https://openalex.org/C2780022179","wikidata":"https://www.wikidata.org/wiki/Q1986794","display_name":"Molecular graph","level":3,"score":0.5006959438323975},{"id":"https://openalex.org/C511192102","wikidata":"https://www.wikidata.org/wiki/Q5156948","display_name":"Comprehension","level":2,"score":0.492591917514801},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.41657066345214844},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.407585084438324},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39118900895118713},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.20589479804039001},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.12608671188354492},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"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:10.1145/3637528.3672043","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3637528.3672043","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3637528.3672043","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 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3637528.3672043","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3637528.3672043","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3637528.3672043","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 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4401857142.pdf"},"referenced_works_count":34,"referenced_works":["https://openalex.org/W1593271688","https://openalex.org/W1963637997","https://openalex.org/W2020278455","https://openalex.org/W2158219301","https://openalex.org/W2177317049","https://openalex.org/W2292734588","https://openalex.org/W2418258728","https://openalex.org/W2600463316","https://openalex.org/W2767891136","https://openalex.org/W2888236192","https://openalex.org/W2889326414","https://openalex.org/W3015453090","https://openalex.org/W3113317199","https://openalex.org/W3122451732","https://openalex.org/W3151929433","https://openalex.org/W3173151551","https://openalex.org/W3173169192","https://openalex.org/W3198511414","https://openalex.org/W3211951295","https://openalex.org/W4212774754","https://openalex.org/W4213077304","https://openalex.org/W4214868967","https://openalex.org/W4226159083","https://openalex.org/W4290875097","https://openalex.org/W4293416771","https://openalex.org/W4306317004","https://openalex.org/W4323304388","https://openalex.org/W4381679608","https://openalex.org/W4385567824","https://openalex.org/W4385572894","https://openalex.org/W4389524207","https://openalex.org/W4389888290","https://openalex.org/W4392357044","https://openalex.org/W4396622252"],"related_works":["https://openalex.org/W2616627668","https://openalex.org/W3137121595","https://openalex.org/W2062195135","https://openalex.org/W2051345519","https://openalex.org/W2102157173","https://openalex.org/W1991530724","https://openalex.org/W3083087975","https://openalex.org/W2023344535","https://openalex.org/W2383982204","https://openalex.org/W2087032481"],"abstract_inverted_index":{"Capturing":[0],"molecular":[1,24,34,49,61,74,81,113,142,154],"knowledge":[2,62,87,93,95],"with":[3],"representation":[4,25,75],"learning":[5,47,76],"approaches":[6],"holds":[7],"significant":[8],"potential":[9],"in":[10,46,54,150],"vast":[11],"scientific":[12],"fields":[13],"such":[14],"as":[15],"chemistry":[16],"and":[17,22,32,39,59,91,105,127,156,159],"life":[18],"science.":[19],"An":[20],"effective":[21],"generalizable":[23],"is":[26],"expected":[27],"to":[28,52,101,110],"capture":[29],"the":[30],"consensus":[31],"complementary":[33],"expertise":[35,82],"from":[36,63,83,88,94],"diverse":[37],"views":[38],"perspectives.":[40],"However,":[41],"existing":[42],"works":[43],"fall":[44],"short":[45],"multi-view":[48,80],"representations,":[50],"due":[51],"challenges":[53],"explicitly":[55],"incorporating":[56],"view":[57,103],"information":[58,104],"handling":[60],"heterogeneous":[64,122],"sources.":[65],"To":[66],"address":[67],"these":[68],"issues,":[69],"we":[70,132],"present":[71],"MV-Mol,":[72],"a":[73,107,117],"model":[77,102],"that":[78,134,139],"harvests":[79],"chemical":[84],"structures,":[85],"unstructured":[86],"biomedical":[89],"texts,":[90],"structured":[92],"graphs.":[96],"We":[97,115],"utilize":[98],"text":[99],"prompts":[100],"design":[106],"fusion":[108],"architecture":[109],"extract":[111],"view-based":[112],"representations.":[114],"develop":[116],"two-stage":[118],"pre-training":[119],"procedure,":[120],"exploiting":[121],"data":[123,160],"of":[124,153],"varying":[125],"quality":[126],"quantity.":[128],"Through":[129],"extensive":[130],"experiments,":[131],"show":[133],"MV-Mol":[135,146],"provides":[136],"improved":[137],"representations":[138],"substantially":[140],"benefit":[141],"property":[143],"prediction.":[144],"Additionally,":[145],"exhibits":[147],"state-of-the-art":[148],"performance":[149],"multi-modal":[151],"comprehension":[152],"structures":[155],"texts.":[157],"Code":[158],"are":[161],"available":[162],"at":[163],"https://github.com/PharMolix/OpenBioMed.":[164]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":7}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}