{"id":"https://openalex.org/W4385567824","doi":"https://doi.org/10.1145/3580305.3599317","title":"Dual-view Molecular Pre-training","display_name":"Dual-view Molecular Pre-training","publication_year":2023,"publication_date":"2023-08-04","ids":{"openalex":"https://openalex.org/W4385567824","doi":"https://doi.org/10.1145/3580305.3599317"},"language":"en","primary_location":{"is_oa":true,"landing_page_url":"https://doi.org/10.1145/3580305.3599317","pdf_url":null,"source":{"id":"https://openalex.org/S4363608767","display_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true},"type":"article","type_crossref":"proceedings-article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":"https://doi.org/10.1145/3580305.3599317","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5028284977","display_name":"Jinhua Zhu","orcid":"https://orcid.org/0000-0003-2157-9077"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinhua Zhu","raw_affiliation_strings":["University of Science and Technology of China, Hefei, China"],"affiliations":[{"raw_affiliation_string":"University of Science and Technology of China, Hefei, China","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021772140","display_name":"Yingce Xia","orcid":"https://orcid.org/0000-0001-9823-9033"},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yingce Xia","raw_affiliation_strings":["Microsoft Research AI4Science, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Microsoft Research AI4Science, Beijing, China","institution_ids":["https://openalex.org/I4210113369"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102750692","display_name":"Lijun Wu","orcid":"https://orcid.org/0000-0002-3530-590X"},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lijun Wu","raw_affiliation_strings":["Microsoft Research AI4Science, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Microsoft Research AI4Science, Beijing, China","institution_ids":["https://openalex.org/I4210113369"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5088666942","display_name":"Shufang Xie","orcid":"https://orcid.org/0000-0002-7126-0139"},"institutions":[{"id":"https://openalex.org/I78988378","display_name":"Renmin University of China","ror":"https://ror.org/041pakw92","country_code":"CN","type":"education","lineage":["https://openalex.org/I78988378"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shufang Xie","raw_affiliation_strings":["Renmin University of China, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Renmin University of China, Beijing, China","institution_ids":["https://openalex.org/I78988378"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5046805800","display_name":"Wengang Zhou","orcid":"https://orcid.org/0000-0003-1690-9836"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wengang Zhou","raw_affiliation_strings":["University of Science and Technology of China, Hefei, China"],"affiliations":[{"raw_affiliation_string":"University of Science and Technology of China, Hefei, China","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020025718","display_name":"Tao Qin","orcid":"https://orcid.org/0000-0002-9095-0776"},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tao Qin","raw_affiliation_strings":["Microsoft Research AI4Science, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Microsoft Research AI4Science, Beijing, China","institution_ids":["https://openalex.org/I4210113369"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078141810","display_name":"Houqiang Li","orcid":"https://orcid.org/0000-0003-2188-3028"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Houqiang Li","raw_affiliation_strings":["University of Science and Technology of China, Hefei, China"],"affiliations":[{"raw_affiliation_string":"University of Science and Technology of China, Hefei, China","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101884287","display_name":"Tie\u2010Yan Liu","orcid":"https://orcid.org/0000-0002-0476-8020"},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tie-Yan Liu","raw_affiliation_strings":["Microsoft Research AI4Science, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Microsoft Research AI4Science, Beijing, China","institution_ids":["https://openalex.org/I4210113369"]}]}],"institution_assertions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.8,"has_fulltext":true,"fulltext_origin":"pdf","cited_by_count":13,"citation_normalized_percentile":{"value":0.99997,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"3615","last_page":"3627"},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T11948","display_name":"Accelerating Materials Innovation through Informatics","score":0.9998,"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"}},"topics":[{"id":"https://openalex.org/T11948","display_name":"Accelerating Materials Innovation through Informatics","score":0.9998,"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/T10211","display_name":"Computational Methods in Drug Discovery","score":0.9997,"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/T10044","display_name":"Protein Structure Prediction and Analysis","score":0.9894,"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/molecular-graph","display_name":"Molecular graph","score":0.6194881},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.5631028},{"id":"https://openalex.org/keywords/cheminformatics","display_name":"Cheminformatics","score":0.55945385},{"id":"https://openalex.org/keywords/molecular-docking","display_name":"Molecular Docking","score":0.53504},{"id":"https://openalex.org/keywords/molecular-dynamics","display_name":"Molecular Dynamics","score":0.519764},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.47105193}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.76231986},{"id":"https://openalex.org/C2780022179","wikidata":"https://www.wikidata.org/wiki/Q1986794","display_name":"Molecular graph","level":3,"score":0.6194881},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.5631028},{"id":"https://openalex.org/C68762167","wikidata":"https://www.wikidata.org/wiki/Q910164","display_name":"Cheminformatics","level":2,"score":0.55945385},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.5541102},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.5218436},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.47105193},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4659762},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.42429924},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4014868},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.39524612},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.11404389},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.08927411},{"id":"https://openalex.org/C147597530","wikidata":"https://www.wikidata.org/wiki/Q369472","display_name":"Computational chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"is_oa":true,"landing_page_url":"https://doi.org/10.1145/3580305.3599317","pdf_url":null,"source":{"id":"https://openalex.org/S4363608767","display_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true}],"best_oa_location":{"is_oa":true,"landing_page_url":"https://doi.org/10.1145/3580305.3599317","pdf_url":null,"source":{"id":"https://openalex.org/S4363608767","display_name":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true},"sustainable_development_goals":[],"grants":[{"funder":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China","award_id":"61836011,62021001"}],"datasets":[],"versions":[],"referenced_works_count":25,"referenced_works":["https://openalex.org/W1975147762","https://openalex.org/W2048679005","https://openalex.org/W2051224630","https://openalex.org/W2138621090","https://openalex.org/W2187089797","https://openalex.org/W2194775991","https://openalex.org/W2268755124","https://openalex.org/W2943495267","https://openalex.org/W2951433247","https://openalex.org/W2966357564","https://openalex.org/W2973114758","https://openalex.org/W3035524453","https://openalex.org/W3087318293","https://openalex.org/W3095883070","https://openalex.org/W3102659967","https://openalex.org/W3174318304","https://openalex.org/W3189262114","https://openalex.org/W3211876185","https://openalex.org/W4213077304","https://openalex.org/W4214868967","https://openalex.org/W4229040393","https://openalex.org/W4256300792","https://openalex.org/W4281706128","https://openalex.org/W4283687058","https://openalex.org/W4286901673"],"related_works":["https://openalex.org/W4387608223","https://openalex.org/W4386509167","https://openalex.org/W4293771607","https://openalex.org/W3165034028","https://openalex.org/W3093734399","https://openalex.org/W2904656109","https://openalex.org/W2889938001","https://openalex.org/W2290847742","https://openalex.org/W1573015311","https://openalex.org/W1570419641"],"abstract_inverted_index":{"Molecular":[0],"pre-training,":[1,146],"which":[2,76],"is":[3,46,156,171,198],"about":[4],"to":[5,48,66,88,96,138,159],"learn":[6],"an":[7],"effective":[8],"representation":[9],"for":[10,167],"molecules":[11,83],"on":[12,173,187],"large":[13],"amount":[14],"of":[15,117,120,143],"data,":[16,75],"has":[17,124],"attracted":[18],"substantial":[19],"attention":[20],"in":[21],"cheminformatics":[22],"and":[23,57,72,79,85,100,128,132,179,191],"bioinformatics.":[24],"A":[25],"molecule":[26,107,121],"can":[27,77,112,148],"be":[28,89],"viewed":[29],"as":[30],"either":[31,150],"a":[32,41,102,125,129],"graph":[33,51,58],"(where":[34,44],"atoms":[35],"are":[36,62,86,136],"connected":[37],"by":[38],"bonds)":[39],"or":[40,165],"SMILES":[42],"sequence":[43],"depth-first-search":[45],"applied":[47],"the":[49,69,73,82,115,133,140,151,162],"molecular":[50,175],"with":[52,68],"specific":[53],"rules).":[54],"The":[55],"Transformer":[56,126,152],"neural":[59],"networks":[60],"(GNN)":[61],"two":[63,134],"representative":[64],"methods":[65],"deal":[67],"sequential":[70],"data":[71],"graphic":[74],"globally":[78],"locally":[80],"model":[81],"respectively":[84],"supposed":[87],"complementary.":[90],"In":[91],"this":[92],"work,":[93],"we":[94,147,184],"propose":[95],"leverage":[97],"both":[98,118,166],"representations":[99],"design":[101],"new":[103],"pre-training":[104,108],"algorithm,":[105],"dual-view":[106],"(briefly,":[109],"DVMP),":[110],"that":[111],"effectively":[113],"combine":[114],"strengths":[116],"types":[119],"representations.":[122],"DVMP":[123,170,186],"branch":[127,153],"GNN":[130,163],"branch,":[131,164],"branches":[135],"pre-trained":[137],"maintain":[139],"semantic":[141],"consistency":[142],"molecules.":[144],"After":[145],"use":[149],"(this":[154],"one":[155],"recommended":[157],"according":[158],"empirical":[160],"results),":[161],"downstream":[168],"tasks.":[169],"tested":[172],"11":[174],"property":[176],"prediction":[177],"tasks":[178,190],"outperforms":[180],"strong":[181],"baselines.":[182],"Furthermore,":[183],"test":[185],"three":[188],"retrosynthesis":[189],"it":[192],"achieves":[193],"state-of-the-art":[194],"results.":[195],"Our":[196],"code":[197],"released":[199],"at":[200],"https://github.com/microsoft/DVMP.":[201]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4385567824","counts_by_year":[{"year":2024,"cited_by_count":11},{"year":2023,"cited_by_count":2}],"updated_date":"2024-12-03T08:24:10.748937","created_date":"2023-08-05"}