{"id":"https://openalex.org/W7204264249","doi":"https://doi.org/10.48550/arxiv.2608.24610","title":"Conditional GraphGANFed: Optimizing Graph-Structured Molecule Generation in Federated Generative Adversarial Networks","display_name":"Conditional GraphGANFed: Optimizing Graph-Structured Molecule Generation in Federated Generative Adversarial Networks","publication_year":2026,"publication_date":"2026-08-24","ids":{"openalex":"https://openalex.org/W7204264249","doi":"https://doi.org/10.48550/arxiv.2608.24610"},"language":null,"primary_location":{"id":"pmh:oai:arXiv.org:2608.24610","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2608.24610","pdf_url":"https://arxiv.org/pdf/2608.24610","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2608.24610","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5050337587","display_name":"Daniel Manu","orcid":"https://orcid.org/0000-0001-5498-2677","observed_orcids":["https://orcid.org/0000-0001-5498-2677"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Manu, Daniel","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5148393586","display_name":"Abee Alazzwi","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Alazzwi, Abee","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10211","display_name":"Computational Drug Discovery Methods","score":0.9585000276565552,"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":0.9585000276565552,"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/T10764","display_name":"Privacy-Preserving Technologies in Data","score":0.017799999564886093,"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"}},{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.0071000000461936,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/conditional-gan","display_name":"conditional generative adversarial networks","score":0.6710000038146973},{"id":"https://openalex.org/keywords/federated-learning","display_name":"federated learning","score":0.878000020980835},{"id":"https://openalex.org/keywords/graph-convolutional-networks","display_name":"graph convolutional networks","score":0.8009999990463257},{"id":"https://openalex.org/keywords/generative-adversarial-networks","display_name":"generative adversarial networks","score":0.7749999761581421},{"id":"https://openalex.org/keywords/drug-discovery","display_name":"drug discovery","score":0.6549999713897705},{"id":"https://openalex.org/keywords/data-privacy","display_name":"data privacy","score":0.8059999942779541},{"id":"https://openalex.org/keywords/quantum-electrodynamics","display_name":"quantum electrodynamics","score":0.6389999985694885},{"id":"https://openalex.org/keywords/mode-collapse","display_name":"mode collapse","score":0.7400000095367432},{"id":"https://openalex.org/keywords/non-iid-data","display_name":"non-IID data","score":0.9399999976158142}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7731000185012817},{"id":"https://openalex.org/C2779803651","wikidata":"https://www.wikidata.org/wiki/Q5282088","display_name":"Discriminator","level":3,"score":0.6556000113487244},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.46399998664855957},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.43529999256134033},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.41999998688697815},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.3903000056743622},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.36910000443458557},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.36629998683929443},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.36579999327659607},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.357699990272522},{"id":"https://openalex.org/C2988773926","wikidata":"https://www.wikidata.org/wiki/Q25104379","display_name":"Generative adversarial network","level":3,"score":0.33660000562667847},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3109000027179718},{"id":"https://openalex.org/C2778029271","wikidata":"https://www.wikidata.org/wiki/Q5421931","display_name":"Extension (predicate logic)","level":2,"score":0.30790001153945923},{"id":"https://openalex.org/C2779585090","wikidata":"https://www.wikidata.org/wiki/Q3457762","display_name":"Resilience (materials science)","level":2,"score":0.29739999771118164},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.2890999913215637},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.2784000039100647},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.2782000005245209},{"id":"https://openalex.org/C48677424","wikidata":"https://www.wikidata.org/wiki/Q6888088","display_name":"Mode (computer interface)","level":2,"score":0.2606000006198883},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.2572000026702881},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.25619998574256897}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2608.24610","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2608.24610","pdf_url":"https://arxiv.org/pdf/2608.24610","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2608.24610","doi":"https://doi.org/10.48550/arxiv.2608.24610","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2608.24610","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2608.24610","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2608.24610","pdf_url":"https://arxiv.org/pdf/2608.24610","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"sustainable_development_goals_aurora":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.691612184047699}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7204264249.pdf","grobid_xml":"https://content.openalex.org/works/W7204264249.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Generative":[0],"adversarial":[1],"networks":[2,41],"(GANs)":[3],"have":[4],"garnered":[5],"considerable":[6],"attention":[7],"in":[8,131,153,161,190],"molecular":[9],"discovery":[10,61],"for":[11],"their":[12],"ability":[13],"to":[14,34,56,72,83,111,120,137],"generate":[15,112],"novel":[16,70,113],"and":[17,38,98,144,155,173,205],"high-quality":[18],"molecules.":[19],"To":[20,63],"efficiently":[21],"train":[22],"a":[23,53,69,158],"GAN":[24],"model":[25],"while":[26,123],"preserving":[27],"data":[28],"privacy,":[29],"GraphGANFed":[30,45,76,152],"has":[31,199],"been":[32],"proposed":[33],"incorporate":[35],"federated":[36],"learning":[37],"graph":[39],"convolutional":[40],"into":[42,102],"GAN.":[43],"Yet,":[44],"cannot":[46],"produce":[47],"synthetic":[48,179],"molecules":[49,86,114,180],"that":[50,115,148,177],"only":[51],"optimize":[52,138],"user-defined":[54,88,125],"metric(s)":[55],"facilitate":[57],"the":[58,80,95,103,107,145,174,178,195],"new":[59],"drug":[60],"process.":[62],"address":[64],"this":[65],"issue,":[66],"we":[67],"introduce":[68],"extension":[71],"GraphGANFed,":[73],"namely":[74],"conditional":[75],"(cGraphGANFed),":[77],"by":[78,182,209],"incorporating":[79],"critic":[81,96],"network":[82,97],"assess":[84],"generated":[85],"using":[87],"metric(s).":[89],"The":[90],"evaluation":[91],"results":[92,146,175,196],"from":[93],"both":[94],"discriminator":[99],"are":[100,129],"integrated":[101],"loss":[104],"function":[105],"of":[106],"generator,":[108],"guiding":[109],"it":[110],"maintain":[116],"similar":[117],"chemical":[118],"properties":[119],"real":[121],"ones":[122],"optimizing":[124,171],"metrics.":[126],"Extensive":[127],"simulations":[128],"conducted":[130],"two":[132],"scenarios.":[133],"First,":[134],"cGraphGANFed":[135,149,167,183,198],"endeavors":[136],"all":[139],"seven":[140],"commonly":[141],"used":[142],"metrics,":[143],"show":[147,176],"significantly":[150],"outperforms":[151],"Validity":[154],"LogP,":[156],"with":[157],"slight":[159],"advantage":[160],"QED,":[162,172],"across":[163],"different":[164],"settings.":[165],"Second,":[166],"focuses":[168],"solely":[169],"on":[170],"produced":[181],"can":[184],"achieve":[185],"more":[186],"than":[187,192],"10%":[188],"improvement":[189],"QED":[191],"GraphGANFed.":[193],"Also,":[194],"demonstrate":[197],"enhanced":[200],"resilience":[201],"against":[202],"mode":[203],"collapses":[204],"performance":[206],"reduction":[207],"caused":[208],"non-IID":[210],"data.":[211]},"counts_by_year":[],"updated_date":"2026-09-16T18:09:00.828671","created_date":"2026-08-27T00:00:00"}
