{"id":"https://openalex.org/W4417210802","doi":"https://doi.org/10.48550/arxiv.2511.14516","title":"Full-Atom Peptide Design via Riemannian-Euclidean Bayesian Flow Networks","display_name":"Full-Atom Peptide Design via Riemannian-Euclidean Bayesian Flow Networks","publication_year":2025,"publication_date":"2025-11-18","ids":{"openalex":"https://openalex.org/W4417210802","doi":"https://doi.org/10.48550/arxiv.2511.14516"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2511.14516","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2511.14516","pdf_url":"https://arxiv.org/pdf/2511.14516","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2511.14516","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101568344","display_name":"Hao Qian","orcid":"https://orcid.org/0000-0001-7840-0520"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qian, Hao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5073963268","display_name":"Shikui Tu","orcid":"https://orcid.org/0000-0001-6270-0449"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tu, Shikui","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5101662875","display_name":"Lei Xu","orcid":"https://orcid.org/0000-0003-4611-0910"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Lei","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/T10911","display_name":"Chemical Synthesis and Analysis","score":0.8319000005722046,"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/T10911","display_name":"Chemical Synthesis and Analysis","score":0.8319000005722046,"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/T11419","display_name":"Supramolecular Self-Assembly in Materials","score":0.026900000870227814,"subfield":{"id":"https://openalex.org/subfields/2502","display_name":"Biomaterials"},"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/T10044","display_name":"Protein Structure and Dynamics","score":0.017400000244379044,"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/bayesian-probability","display_name":"Bayesian probability","score":0.6385999917984009},{"id":"https://openalex.org/keywords/categorical-variable","display_name":"Categorical variable","score":0.5817000269889832},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4699999988079071},{"id":"https://openalex.org/keywords/bayesian-network","display_name":"Bayesian network","score":0.4652999937534332},{"id":"https://openalex.org/keywords/bayes-theorem","display_name":"Bayes' theorem","score":0.3977000117301941},{"id":"https://openalex.org/keywords/flow","display_name":"Flow (mathematics)","score":0.37229999899864197},{"id":"https://openalex.org/keywords/joint-probability-distribution","display_name":"Joint probability distribution","score":0.33500000834465027},{"id":"https://openalex.org/keywords/mixture-model","display_name":"Mixture model","score":0.32249999046325684},{"id":"https://openalex.org/keywords/probability-distribution","display_name":"Probability distribution","score":0.30979999899864197}],"concepts":[{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.6385999917984009},{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.5817000269889832},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5318999886512756},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.49399998784065247},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4699999988079071},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.4652999937534332},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.3977000117301941},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.37229999899864197},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.35920000076293945},{"id":"https://openalex.org/C186060115","wikidata":"https://www.wikidata.org/wiki/Q30336093","display_name":"Biological system","level":1,"score":0.35670000314712524},{"id":"https://openalex.org/C18653775","wikidata":"https://www.wikidata.org/wiki/Q1333358","display_name":"Joint probability distribution","level":2,"score":0.33500000834465027},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.33219999074935913},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.32249999046325684},{"id":"https://openalex.org/C121864883","wikidata":"https://www.wikidata.org/wiki/Q677916","display_name":"Statistical physics","level":1,"score":0.3116999864578247},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.30979999899864197},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.3091000020503998},{"id":"https://openalex.org/C71983512","wikidata":"https://www.wikidata.org/wiki/Q7915687","display_name":"Variable-order Bayesian network","level":4,"score":0.3041999936103821},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.2994999885559082},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.29440000653266907},{"id":"https://openalex.org/C2985264121","wikidata":"https://www.wikidata.org/wiki/Q216320","display_name":"Continuous flow","level":2,"score":0.28679999709129333},{"id":"https://openalex.org/C73301696","wikidata":"https://www.wikidata.org/wiki/Q5469984","display_name":"Formalism (music)","level":3,"score":0.2831000089645386},{"id":"https://openalex.org/C2983394010","wikidata":"https://www.wikidata.org/wiki/Q19885330","display_name":"Continuous variable","level":2,"score":0.27950000762939453},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.27709999680519104},{"id":"https://openalex.org/C10184394","wikidata":"https://www.wikidata.org/wiki/Q5165491","display_name":"Continuous modelling","level":2,"score":0.26669999957084656},{"id":"https://openalex.org/C55689738","wikidata":"https://www.wikidata.org/wiki/Q15963867","display_name":"Discrete time and continuous time","level":2,"score":0.26179999113082886},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2558000087738037},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.2556999921798706},{"id":"https://openalex.org/C111350023","wikidata":"https://www.wikidata.org/wiki/Q1191869","display_name":"Markov chain Monte Carlo","level":3,"score":0.2526000142097473}],"mesh":[],"locations_count":3,"locations":[{"id":"pmh:oai:arXiv.org:2511.14516","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2511.14516","pdf_url":"https://arxiv.org/pdf/2511.14516","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:ojs.aaai.org:article/39679","is_oa":false,"landing_page_url":"https://ojs.aaai.org/index.php/AAAI/article/view/39679","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"2159-5399","raw_type":"info:eu-repo/semantics/article"},{"id":"doi:10.48550/arxiv.2511.14516","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2511.14516","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":"pmh:oai:arXiv.org:2511.14516","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2511.14516","pdf_url":"https://arxiv.org/pdf/2511.14516","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"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":{"Diffusion":[0],"and":[1,57,84,128,155,183,194],"flow":[2,98,146,161],"matching":[3],"models":[4,19,64,107,116],"have":[5],"recently":[6],"emerged":[7],"as":[8],"promising":[9],"approaches":[10],"for":[11,68,100],"peptide":[12,103,185,206],"binder":[13,195],"design.":[14,207],"Despite":[15],"their":[16,33,122],"progress,":[17],"these":[18,90,172],"still":[20],"face":[21],"two":[22],"major":[23],"challenges.":[24],"First,":[25],"categorical":[26],"sampling":[27],"of":[28,79,202],"discrete":[29,117],"residue":[30,118,165],"types":[31,119],"collapses":[32],"continuous":[34,40,112,123,134],"parameters":[35],"into":[36],"onehot":[37],"assignments,":[38],"while":[39],"variables":[41],"(e.g.,":[42],"atom":[43,102],"positions)":[44],"evolve":[45],"smoothly":[46],"throughout":[47],"the":[48,54,75,95,149,168,199],"generation":[49],"process.":[50],"This":[51],"mismatch":[52],"disrupts":[53],"update":[55],"dynamics":[56],"results":[58],"in":[59,110,204],"suboptimal":[60],"performance.":[61],"Second,":[62],"current":[63],"assume":[65],"unimodal":[66],"distributions":[67,109,174],"side-chain":[69],"torsion":[70],"angles,":[71],"which":[72],"conflicts":[73],"with":[74,132],"inherently":[76],"multimodal":[77,150],"nature":[78],"side":[80,151,189],"chain":[81,152,190],"rotameric":[82,153],"states":[83,154],"limits":[85],"prediction":[86],"accuracy.":[87],"To":[88],"address":[89],"limitations,":[91],"we":[92],"introduce":[93],"PepBFN,":[94],"first":[96],"Bayesian":[97,130,145,179],"network":[99],"full":[101],"design":[104,196],"that":[105],"directly":[106,163],"parameter":[108,124,173],"fully":[111],"space.":[113],"Specifically,":[114],"PepBFN":[115,203],"by":[120],"learning":[121],"distributions,":[125],"enabling":[126],"joint":[127],"smooth":[129,182],"updates":[131],"other":[133],"structural":[135],"parameters.":[136],"It":[137],"further":[138],"employs":[139],"a":[140,156],"novel":[141],"Gaussian":[142],"mixture":[143],"based":[144,159],"to":[147,162],"capture":[148],"Matrix":[157],"Fisher":[158],"Riemannian":[160],"model":[164],"orientations":[166],"on":[167,188],"$\\mathrm{SO}(3)$":[169],"manifold.":[170],"Together,":[171],"are":[175],"progressively":[176],"refined":[177],"via":[178],"updates,":[180],"yielding":[181],"coherent":[184],"generation.":[186],"Experiments":[187],"packing,":[191],"reverse":[192],"folding,":[193],"tasks":[197],"demonstrate":[198],"strong":[200],"potential":[201],"computational":[205]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2025-11-20T00:00:00"}
