{"id":"https://openalex.org/W7133191678","doi":"https://doi.org/10.48550/arxiv.2602.24007","title":"Inference-time optimization for experiment-grounded protein ensemble generation","display_name":"Inference-time optimization for experiment-grounded protein ensemble generation","publication_year":2026,"publication_date":"2026-02-27","ids":{"openalex":"https://openalex.org/W7133191678","doi":"https://doi.org/10.48550/arxiv.2602.24007"},"language":null,"primary_location":{"id":"pmh:doi:10.48550/arxiv.2602.24007","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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":"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":"Article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":null,"any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5116276037","display_name":"Advaith Maddipatla","orcid":"https://orcid.org/0000-0002-1058-6012"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Maddipatla, Advaith","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127841728","display_name":"Anar Rzayev","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rzayev, Anar","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042020426","display_name":"Marco Pegoraro","orcid":"https://orcid.org/0000-0001-5690-8403"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pegoraro, Marco","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127824439","display_name":"Martin Pacesa","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pacesa, Martin","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127789579","display_name":"Paul Schanda","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Schanda, Paul","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059089284","display_name":"Ailie Marx","orcid":"https://orcid.org/0000-0001-9399-2819"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Marx, Ailie","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087529022","display_name":"Sanketh Vedula","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Vedula, Sanketh","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5127782985","display_name":"Alex M. Bronstein","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bronstein, Alex M.","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5116276037"],"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/T10044","display_name":"Protein Structure and Dynamics","score":0.8797000050544739,"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"}},"topics":[{"id":"https://openalex.org/T10044","display_name":"Protein Structure and Dynamics","score":0.8797000050544739,"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"}},{"id":"https://openalex.org/T11016","display_name":"Monoclonal and Polyclonal Antibodies Research","score":0.026599999517202377,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T13326","display_name":"Biochemical and Structural Characterization","score":0.0210999995470047,"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/initialization","display_name":"Initialization","score":0.7839000225067139},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.5773000121116638},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5267999768257141},{"id":"https://openalex.org/keywords/current","display_name":"Current (fluid)","score":0.5091999769210815},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.3991999924182892},{"id":"https://openalex.org/keywords/sensitivity","display_name":"Sensitivity (control systems)","score":0.3894999921321869},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.383899986743927},{"id":"https://openalex.org/keywords/experimental-data","display_name":"Experimental data","score":0.3815999925136566},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.3709000051021576}],"concepts":[{"id":"https://openalex.org/C114466953","wikidata":"https://www.wikidata.org/wiki/Q6034165","display_name":"Initialization","level":2,"score":0.7839000225067139},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6887999773025513},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.5773000121116638},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5267999768257141},{"id":"https://openalex.org/C148043351","wikidata":"https://www.wikidata.org/wiki/Q4456944","display_name":"Current (fluid)","level":2,"score":0.5091999769210815},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.507099986076355},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.3991999924182892},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.390500009059906},{"id":"https://openalex.org/C21200559","wikidata":"https://www.wikidata.org/wiki/Q7451068","display_name":"Sensitivity (control systems)","level":2,"score":0.3894999921321869},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.383899986743927},{"id":"https://openalex.org/C55037315","wikidata":"https://www.wikidata.org/wiki/Q5421151","display_name":"Experimental data","level":2,"score":0.3815999925136566},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.3709000051021576},{"id":"https://openalex.org/C69357855","wikidata":"https://www.wikidata.org/wiki/Q163214","display_name":"Diffusion","level":2,"score":0.3700999915599823},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.3458000123500824},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.34220001101493835},{"id":"https://openalex.org/C90673727","wikidata":"https://www.wikidata.org/wiki/Q901718","display_name":"Product (mathematics)","level":2,"score":0.3253999948501587},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32519999146461487},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32089999318122864},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.30550000071525574},{"id":"https://openalex.org/C158424031","wikidata":"https://www.wikidata.org/wiki/Q1191905","display_name":"Gibbs sampling","level":3,"score":0.3012000024318695},{"id":"https://openalex.org/C129848803","wikidata":"https://www.wikidata.org/wiki/Q2564360","display_name":"Sample size determination","level":2,"score":0.3003000020980835},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.2980000078678131},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2948000133037567},{"id":"https://openalex.org/C24552861","wikidata":"https://www.wikidata.org/wiki/Q2670177","display_name":"Data assimilation","level":2,"score":0.29089999198913574},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.28679999709129333},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.2743000090122223},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.26980000734329224},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.26330000162124634},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.2623000144958496},{"id":"https://openalex.org/C186060115","wikidata":"https://www.wikidata.org/wiki/Q30336093","display_name":"Biological system","level":1,"score":0.2621999979019165},{"id":"https://openalex.org/C34559072","wikidata":"https://www.wikidata.org/wiki/Q2334061","display_name":"Design of experiments","level":2,"score":0.26159998774528503},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.2615000009536743},{"id":"https://openalex.org/C95713431","wikidata":"https://www.wikidata.org/wiki/Q631425","display_name":"Vulnerability (computing)","level":2,"score":0.25029999017715454}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.48550/arxiv.2602.24007","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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":"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":"Article"},{"id":"doi:10.48550/arxiv.2602.24007","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.24007","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":"article"}],"best_oa_location":{"id":"pmh:doi:10.48550/arxiv.2602.24007","is_oa":true,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","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":"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":"Article"},"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":{"Protein":[0],"function":[1],"relies":[2],"on":[3,84],"dynamic":[4],"conformational":[5],"ensembles,":[6],"yet":[7],"current":[8,179],"generative":[9],"models":[10],"like":[11],"AlphaFold3":[12,110,167],"often":[13,48,147],"fail":[14],"to":[15,26,46,60,70,188],"produce":[16],"ensembles":[17],"that":[18,127,165],"match":[19],"experimental":[20,122,150],"data.":[21],"Recent":[22],"experiment-guided":[23],"generators":[24],"attempt":[25],"address":[27],"this":[28,128],"by":[29,40],"steering":[30],"the":[31,149],"reverse":[32],"diffusion":[33,85],"process.":[34],"However,":[35],"these":[36,62],"methods":[37],"are":[38],"limited":[39],"fixed":[41],"sampling":[42,99],"horizons":[43],"and":[44,90,138,145],"sensitivity":[45],"initialization,":[47],"yielding":[49],"thermodynamically":[50],"implausible":[51],"results.":[52],"We":[53],"introduce":[54],"a":[55,176,186],"general":[56],"inference-time":[57,158],"optimization":[58,159],"framework":[59,129],"solve":[61],"challenges.":[63],"First,":[64],"we":[65,96,114],"optimize":[66],"over":[67],"latent":[68],"representations":[69],"maximize":[71],"ensemble":[72],"log-likelihood,":[73],"rather":[74],"than":[75,153],"perturbing":[76,166],"structures":[77],"post":[78],"hoc.":[79],"This":[80,174],"approach":[81],"eliminates":[82],"dependence":[83],"length,":[86],"removes":[87],"initialization":[88],"bias,":[89],"easily":[91],"incorporates":[92],"external":[93],"constraints.":[94],"Second,":[95],"present":[97],"novel":[98],"schemes":[100],"for":[101],"drawing":[102],"Boltzmann-weighted":[103],"ensembles.":[104],"By":[105],"combining":[106],"structural":[107],"priors":[108],"from":[109,116],"with":[111,140],"force-field-based":[112],"priors,":[113],"sample":[115],"their":[117],"product":[118],"distribution":[119],"while":[120],"balancing":[121],"likelihoods.":[123],"Our":[124],"results":[125],"show":[126],"consistently":[130],"outperforms":[131],"state-of-the-art":[132],"guidance,":[133],"improving":[134],"diversity,":[135],"physical":[136],"energy,":[137],"agreement":[139],"data":[141,151],"in":[142,178,193],"X-ray":[143],"crystallography":[144],"NMR,":[146],"fitting":[148],"better":[152],"deposited":[154],"PDB":[155],"structures.":[156],"Finally,":[157],"experiments":[160],"maximizing":[161],"ipTM":[162],"scores":[163],"reveal":[164],"embeddings":[168],"can":[169],"artificially":[170],"inflate":[171],"model":[172],"confidence.":[173],"exposes":[175],"vulnerability":[177],"design":[180],"metrics,":[181],"whose":[182],"mitigation":[183],"could":[184],"offer":[185],"pathway":[187],"reduce":[189],"false":[190],"discovery":[191],"rates":[192],"binder":[194],"engineering.":[195]},"counts_by_year":[],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2026-03-03T00:00:00"}
