{"id":"https://openalex.org/W7163033900","doi":"https://doi.org/10.48550/arxiv.2605.30610","title":"Constrained Flow Optimization via Sequential Fine Tuning for Molecular Design","display_name":"Constrained Flow Optimization via Sequential Fine Tuning for Molecular Design","publication_year":2026,"publication_date":"2026-05-28","ids":{"openalex":"https://openalex.org/W7163033900","doi":"https://doi.org/10.48550/arxiv.2605.30610"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.30610","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.30610","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.30610","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5116581840","display_name":"Sven Gutjahr","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gutjahr, Sven","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052185248","display_name":"Riccardo De Santi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"De Santi, Riccardo","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082918690","display_name":"Luca Schaufelberger","orcid":"https://orcid.org/0000-0002-8239-4351"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Schaufelberger, Luca","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084206216","display_name":"Kjell Jorner","orcid":"https://orcid.org/0000-0002-4191-6790"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jorner, Kjell","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137574801","display_name":"Andreas Krause","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Krause, Andreas","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":false,"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/T11948","display_name":"Machine Learning in Materials Science","score":0.2793000042438507,"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":"Machine Learning in Materials Science","score":0.2793000042438507,"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/T11407","display_name":"Innovative Microfluidic and Catalytic Techniques Innovation","score":0.1559000015258789,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10211","display_name":"Computational Drug Discovery Methods","score":0.15160000324249268,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/constraint","display_name":"Constraint (computer-aided design)","score":0.6370000243186951},{"id":"https://openalex.org/keywords/maximization","display_name":"Maximization","score":0.6370000243186951},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.6254000067710876},{"id":"https://openalex.org/keywords/constrained-optimization","display_name":"Constrained optimization","score":0.535099983215332},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.5239999890327454},{"id":"https://openalex.org/keywords/convergence","display_name":"Convergence (economics)","score":0.49059998989105225},{"id":"https://openalex.org/keywords/constraint-satisfaction","display_name":"Constraint satisfaction","score":0.4878000020980835},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.47839999198913574}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7110000252723694},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.6370000243186951},{"id":"https://openalex.org/C2776330181","wikidata":"https://www.wikidata.org/wiki/Q18358244","display_name":"Maximization","level":2,"score":0.6370000243186951},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.6254000067710876},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.5604000091552734},{"id":"https://openalex.org/C55660270","wikidata":"https://www.wikidata.org/wiki/Q5164377","display_name":"Constrained optimization","level":2,"score":0.535099983215332},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.5239999890327454},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.49059998989105225},{"id":"https://openalex.org/C44616089","wikidata":"https://www.wikidata.org/wiki/Q30158686","display_name":"Constraint satisfaction","level":3,"score":0.4878000020980835},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.47839999198913574},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.4684999883174896},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.42480000853538513},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.37229999899864197},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.35030001401901245},{"id":"https://openalex.org/C199622910","wikidata":"https://www.wikidata.org/wiki/Q1128326","display_name":"Constraint satisfaction problem","level":3,"score":0.3386000096797943},{"id":"https://openalex.org/C149672232","wikidata":"https://www.wikidata.org/wiki/Q337048","display_name":"Adaptive optimization","level":2,"score":0.31029999256134033},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3003999888896942},{"id":"https://openalex.org/C2989514635","wikidata":"https://www.wikidata.org/wiki/Q5164377","display_name":"Constrained optimization problem","level":3,"score":0.28700000047683716},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.2815999984741211},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.27630001306533813},{"id":"https://openalex.org/C91575142","wikidata":"https://www.wikidata.org/wiki/Q1971426","display_name":"Optimal control","level":2,"score":0.2741999924182892},{"id":"https://openalex.org/C2775924081","wikidata":"https://www.wikidata.org/wiki/Q55608371","display_name":"Control (management)","level":2,"score":0.273499995470047},{"id":"https://openalex.org/C137105694","wikidata":"https://www.wikidata.org/wiki/Q3407510","display_name":"Local consistency","level":4,"score":0.2597000002861023}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.30610","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.30610","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":"doi:10.48550/arxiv.2605.30610","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.30610","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Adapting":[0],"generative":[1,152,198],"foundation":[2],"models,":[3,9],"in":[4,29,74,185],"particular":[5],"diffusion":[6],"and":[7,57,71,77,104,126,131,154,173],"flow":[8],"to":[10,66,99,139],"optimize":[11],"given":[12],"reward":[13,69,129,186],"functions":[14],"(e.g.,":[15,21],"binding":[16],"affinity)":[17],"while":[18,187],"satisfying":[19],"constraints":[20],"molecular":[22,36,175],"synthesizability)":[23],"is":[24],"fundamental":[25],"for":[26,48,90,150,196],"their":[27],"adoption":[28],"real-world":[30],"scientific":[31],"discovery":[32],"applications":[33],"such":[34,52],"as":[35,112],"design":[37,176],"or":[38],"protein":[39],"engineering.":[40],"While":[41],"recent":[42],"works":[43],"have":[44],"introduced":[45,101],"scalable":[46,144],"methods":[47],"reward-guided":[49],"fine-tuning":[50,141],"of":[51,109,165],"models":[53],"via":[54,142,157],"reinforcement":[55],"learning":[56],"control":[58],"schemes,":[59],"it":[60],"remains":[61],"an":[62,96,122,162],"open":[63],"problem":[64,103,138],"how":[65],"algorithmically":[67],"trade-off":[68],"maximization":[70,130],"constraint":[72,132,190],"satisfaction":[73,133],"a":[75,87,113,174],"reliable":[76],"predictable":[78],"manner.":[79],"Motivated":[80],"by":[81,134],"this":[82],"challenge,":[83],"we":[84,116,160],"first":[85],"present":[86,161],"rigorous":[88],"framework":[89],"Constrained":[91,118],"Generative":[92],"Optimization,":[93],"which":[94],"brings":[95],"optimization":[97,153],"viewpoint":[98],"the":[100,106,136],"adaptation":[102],"retrieves":[105],"relevant":[107],"task":[108],"constrained":[110,151,155,197],"generation":[111,156],"sub-case.":[114],"Then,":[115],"introduce":[117],"Flow":[119],"Optimization":[120],"(CFO),":[121],"algorithm":[123],"that":[124],"automatically":[125],"provably":[127],"balances":[128],"reducing":[135],"original":[137],"sequential":[140],"established,":[143],"methods.":[145],"We":[146],"provide":[147],"convergence":[148],"guarantees":[149],"CFO.":[158],"Ultimately,":[159],"experimental":[163],"evaluation":[164],"CFO":[166,181],"on":[167],"both":[168],"synthetic,":[169],"yet":[170],"illustrative,":[171],"settings,":[172],"task.":[177],"Across":[178],"these":[179],"evaluations,":[180],"achieves":[182],"consistent":[183],"increases":[184],"ensuring":[188],"high":[189],"satisfaction,":[191],"showcasing":[192],"its":[193],"practical":[194],"utility":[195],"optimization.":[199]},"counts_by_year":[],"updated_date":"2026-07-01T08:55:40.977307","created_date":"2026-06-02T00:00:00"}