{"id":"https://openalex.org/W4200163348","doi":"https://doi.org/10.1109/iccad51958.2021.9643440","title":"FL-DISCO: Federated Generative Adversarial Network for Graph-based Molecule Drug Discovery: Special Session Paper","display_name":"FL-DISCO: Federated Generative Adversarial Network for Graph-based Molecule Drug Discovery: Special Session Paper","publication_year":2021,"publication_date":"2021-11-01","ids":{"openalex":"https://openalex.org/W4200163348","doi":"https://doi.org/10.1109/iccad51958.2021.9643440"},"language":"en","primary_location":{"id":"doi:10.1109/iccad51958.2021.9643440","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccad51958.2021.9643440","pdf_url":null,"source":{"id":"https://openalex.org/S4363608354","display_name":"2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD)","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,"raw_source_name":"2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5050337587","display_name":"Daniel Manu","orcid":"https://orcid.org/0000-0001-5498-2677"},"institutions":[{"id":"https://openalex.org/I169521973","display_name":"University of New Mexico","ror":"https://ror.org/05fs6jp91","country_code":"US","type":"education","lineage":["https://openalex.org/I169521973"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Daniel Manu","raw_affiliation_strings":["University of New Mexico, NM, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of New Mexico, NM, USA","institution_ids":["https://openalex.org/I169521973"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100620447","display_name":"Yi Sheng","orcid":"https://orcid.org/0000-0003-0809-6994"},"institutions":[{"id":"https://openalex.org/I162714631","display_name":"George Mason University","ror":"https://ror.org/02jqj7156","country_code":"US","type":"education","lineage":["https://openalex.org/I162714631"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yi Sheng","raw_affiliation_strings":["George Mason University, VA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"George Mason University, VA, USA","institution_ids":["https://openalex.org/I162714631"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070397860","display_name":"Junhuan Yang","orcid":"https://orcid.org/0000-0002-4848-0248"},"institutions":[{"id":"https://openalex.org/I169521973","display_name":"University of New Mexico","ror":"https://ror.org/05fs6jp91","country_code":"US","type":"education","lineage":["https://openalex.org/I169521973"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Junhuan Yang","raw_affiliation_strings":["University of New Mexico, NM, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of New Mexico, NM, USA","institution_ids":["https://openalex.org/I169521973"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080327156","display_name":"Jieren Deng","orcid":"https://orcid.org/0000-0002-5738-0927"},"institutions":[{"id":"https://openalex.org/I140172145","display_name":"University of Connecticut","ror":"https://ror.org/02der9h97","country_code":"US","type":"education","lineage":["https://openalex.org/I140172145"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jieren Deng","raw_affiliation_strings":["University of Connecticut, CT, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Connecticut, CT, USA","institution_ids":["https://openalex.org/I140172145"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078443672","display_name":"Tong Geng","orcid":"https://orcid.org/0000-0002-3644-2922"},"institutions":[{"id":"https://openalex.org/I142606810","display_name":"Pacific Northwest National Laboratory","ror":"https://ror.org/05h992307","country_code":"US","type":"facility","lineage":["https://openalex.org/I1325736334","https://openalex.org/I1330989302","https://openalex.org/I142606810","https://openalex.org/I39565521"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tong Geng","raw_affiliation_strings":["Pacific Northwest National Laboratory, WA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Pacific Northwest National Laboratory, WA, USA","institution_ids":["https://openalex.org/I142606810"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100413657","display_name":"Ang Li","orcid":"https://orcid.org/0000-0003-3734-9137"},"institutions":[{"id":"https://openalex.org/I142606810","display_name":"Pacific Northwest National Laboratory","ror":"https://ror.org/05h992307","country_code":"US","type":"facility","lineage":["https://openalex.org/I1325736334","https://openalex.org/I1330989302","https://openalex.org/I142606810","https://openalex.org/I39565521"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ang Li","raw_affiliation_strings":["Pacific Northwest National Laboratory, WA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Pacific Northwest National Laboratory, WA, USA","institution_ids":["https://openalex.org/I142606810"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030060072","display_name":"Caiwen Ding","orcid":"https://orcid.org/0000-0003-0891-1231"},"institutions":[{"id":"https://openalex.org/I140172145","display_name":"University of Connecticut","ror":"https://ror.org/02der9h97","country_code":"US","type":"education","lineage":["https://openalex.org/I140172145"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Caiwen Ding","raw_affiliation_strings":["University of Connecticut, CT, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Connecticut, CT, USA","institution_ids":["https://openalex.org/I140172145"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019027088","display_name":"Weiwen Jiang","orcid":"https://orcid.org/0000-0002-9004-487X"},"institutions":[{"id":"https://openalex.org/I162714631","display_name":"George Mason University","ror":"https://ror.org/02jqj7156","country_code":"US","type":"education","lineage":["https://openalex.org/I162714631"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Weiwen Jiang","raw_affiliation_strings":["George Mason University, VA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"George Mason University, VA, USA","institution_ids":["https://openalex.org/I162714631"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100698152","display_name":"Lei Yang","orcid":"https://orcid.org/0000-0002-0646-440X"},"institutions":[{"id":"https://openalex.org/I169521973","display_name":"University of New Mexico","ror":"https://ror.org/05fs6jp91","country_code":"US","type":"education","lineage":["https://openalex.org/I169521973"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lei Yang","raw_affiliation_strings":["University of New Mexico, NM, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of New Mexico, NM, USA","institution_ids":["https://openalex.org/I169521973"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10211","display_name":"Computational Drug Discovery Methods","score":1.0,"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":1.0,"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":0.9929999709129333,"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/T12023","display_name":"Cholinesterase and Neurodegenerative Diseases","score":0.9728999733924866,"subfield":{"id":"https://openalex.org/subfields/2736","display_name":"Pharmacology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/discriminator","display_name":"Discriminator","score":0.7610874176025391},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7225606441497803},{"id":"https://openalex.org/keywords/drug-discovery","display_name":"Drug discovery","score":0.6289798021316528},{"id":"https://openalex.org/keywords/session","display_name":"Session (web analytics)","score":0.5367075204849243},{"id":"https://openalex.org/keywords/generative-adversarial-network","display_name":"Generative adversarial network","score":0.5219718217849731},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.507103681564331},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.40874525904655457},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3821274936199188},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.34804460406303406},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.3154333829879761},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.30668580532073975},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.11796599626541138},{"id":"https://openalex.org/keywords/bioinformatics","display_name":"Bioinformatics","score":0.10303589701652527}],"concepts":[{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.7610874176025391},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7225606441497803},{"id":"https://openalex.org/C74187038","wikidata":"https://www.wikidata.org/wiki/Q1418791","display_name":"Drug discovery","level":2,"score":0.6289798021316528},{"id":"https://openalex.org/C2779182362","wikidata":"https://www.wikidata.org/wiki/Q17126187","display_name":"Session (web analytics)","level":2,"score":0.5367075204849243},{"id":"https://openalex.org/C2988773926","wikidata":"https://www.wikidata.org/wiki/Q25104379","display_name":"Generative adversarial network","level":3,"score":0.5219718217849731},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.507103681564331},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40874525904655457},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3821274936199188},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34804460406303406},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3154333829879761},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.30668580532073975},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.11796599626541138},{"id":"https://openalex.org/C60644358","wikidata":"https://www.wikidata.org/wiki/Q128570","display_name":"Bioinformatics","level":1,"score":0.10303589701652527},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iccad51958.2021.9643440","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccad51958.2021.9643440","pdf_url":null,"source":{"id":"https://openalex.org/S4363608354","display_name":"2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD)","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,"raw_source_name":"2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5600000023841858,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W2076498053","https://openalex.org/W2096541451","https://openalex.org/W2397857137","https://openalex.org/W2567534979","https://openalex.org/W2594183968","https://openalex.org/W2618625858","https://openalex.org/W2736137960","https://openalex.org/W2789876780","https://openalex.org/W2806834952","https://openalex.org/W2885195348","https://openalex.org/W2895195713","https://openalex.org/W2911884654","https://openalex.org/W2962953210","https://openalex.org/W2963373786","https://openalex.org/W3005383041","https://openalex.org/W3016142271","https://openalex.org/W3050693196","https://openalex.org/W3087318293","https://openalex.org/W3090369187","https://openalex.org/W3091602279","https://openalex.org/W3092334294","https://openalex.org/W3092618738","https://openalex.org/W3116865743","https://openalex.org/W3121413886","https://openalex.org/W3126767825","https://openalex.org/W3127347132","https://openalex.org/W3128684698","https://openalex.org/W4295521014","https://openalex.org/W4297687186","https://openalex.org/W4297951436","https://openalex.org/W4298289240","https://openalex.org/W4318619660","https://openalex.org/W6729007826","https://openalex.org/W6784001198"],"related_works":["https://openalex.org/W3217069185","https://openalex.org/W3049340819","https://openalex.org/W4308928038","https://openalex.org/W4200430540","https://openalex.org/W3141413246","https://openalex.org/W2808862658","https://openalex.org/W4322709305","https://openalex.org/W3002487853","https://openalex.org/W3010213975","https://openalex.org/W2964074194"],"abstract_inverted_index":{"The":[0],"outbreak":[1],"of":[2,10,48,104,165,222],"the":[3,8,20,42,46,110,125,129,137,163,191,214],"global":[4],"COVID-19":[5],"pandemic":[6],"emphasizes":[7],"importance":[9],"collaborative":[11,36,144],"drug":[12,37,49,63],"discovery":[13,50,64],"for":[14,171,196],"high":[15,183,207],"effectiveness;":[16],"however,":[17],"due":[18],"to":[19,31,34,41,75,85,140,156,190,213],"stringent":[21],"data":[22,24,43,87,91,170,195],"regulation,":[23],"privacy":[25,44,88],"becomes":[26],"an":[27],"imminent":[28],"issue":[29],"needing":[30],"be":[32],"addressed":[33],"enable":[35],"discovery.":[38],"In":[39,132],"addition":[40],"issue,":[45],"efficiency":[47],"is":[51,83,109],"another":[52],"key":[53],"objective":[54],"since":[55],"infectious":[56],"diseases":[57],"spread":[58],"exponentially":[59],"and":[60,113,145,154,173,186,198,209,225],"effectively":[61],"conducting":[62],"could":[65],"save":[66],"lives.":[67],"Advanced":[68],"Artificial":[69],"Intelligence":[70],"(AI)":[71],"techniques":[72],"are":[73],"promising":[74],"solve":[76],"these":[77],"problems:":[78],"(1)":[79,168],"Federated":[80],"Learning":[81],"(FL)":[82],"born":[84],"keep":[86],"while":[89,123],"learning":[90],"from":[92,128],"distributed":[93],"clients;":[94],"(2)":[95,193],"graph":[96],"neural":[97],"network":[98,117],"(GNN)":[99],"can":[100,119,177],"extract":[101],"structural":[102],"properties":[103,126],"molecules":[105,122],"whose":[106],"underlying":[107],"architecture":[108],"connected":[111],"atoms;":[112],"(3)":[114],"generative":[115],"adversarial":[116],"(GAN)":[118],"generate":[120,157,178],"novel":[121,180,204],"retaining":[124],"learned":[127],"training":[130],"data.":[131],"this":[133],"work,":[134],"we":[135],"make":[136],"first":[138],"attempt":[139],"build":[141],"a":[142],"holistic":[143],"privacy-preserving":[146],"FL":[147],"framework,":[148],"namely":[149],"FL-DISCO,":[150],"which":[151],"integrates":[152],"GAN":[153],"GNN":[155],"molecular":[158],"graphs.":[159],"Experimental":[160],"results":[161],"demonstrate":[162,218],"effectiveness":[164],"FL-DISCO":[166,176,201],"on:":[167],"IID":[169],"ESOL":[172,197],"QM9,":[174,199],"where":[175,200],"highly":[179],"compounds":[181,205],"with":[182,206],"drug-likeliness,":[184],"uniqueness":[185],"LogP":[187,210],"scores":[188,211],"compared":[189,212],"baseline;":[192],"non-IID":[194],"generates":[202],"100%":[203],"validity":[208],"baseline.":[215],"We":[216],"also":[217],"how":[219],"different":[220],"fractions":[221],"clients,":[223],"generator":[224],"discriminator":[226],"architectures":[227],"affect":[228],"our":[229],"evaluation":[230],"scores.":[231]},"counts_by_year":[{"year":2025,"cited_by_count":8},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}