{"id":"https://openalex.org/W7138293623","doi":"https://doi.org/10.1609/aaai.v40i29.39679","title":"Full-Atom Peptide Design via Riemannian\u2013Euclidean Bayesian Flow Networks","display_name":"Full-Atom Peptide Design via Riemannian\u2013Euclidean Bayesian Flow Networks","publication_year":2026,"publication_date":"2026-03-14","ids":{"openalex":"https://openalex.org/W7138293623","doi":"https://doi.org/10.1609/aaai.v40i29.39679"},"language":null,"primary_location":{"id":"doi:10.1609/aaai.v40i29.39679","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i29.39679","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.1609/aaai.v40i29.39679","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5129655990","display_name":"Hao Qian","orcid":null},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hao Qian","raw_affiliation_strings":["School of Computer Science, Shanghai Jiao Tong University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, Shanghai Jiao Tong University","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129736341","display_name":"Shikui Tu","orcid":null},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shikui Tu","raw_affiliation_strings":["School of Computer Science, Shanghai Jiao Tong University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, Shanghai Jiao Tong University","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5129651335","display_name":"Lei Xu","orcid":null},"institutions":[{"id":"https://openalex.org/I183067930","display_name":"Shanghai Jiao Tong University","ror":"https://ror.org/0220qvk04","country_code":"CN","type":"education","lineage":["https://openalex.org/I183067930"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lei Xu","raw_affiliation_strings":["School of Computer Science, Shanghai Jiao Tong University\nGuangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Guangdong, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, Shanghai Jiao Tong University\nGuangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Guangdong, China","institution_ids":["https://openalex.org/I183067930"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I183067930"],"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":"40","issue":"29","first_page":"24918","last_page":"24926"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10911","display_name":"Chemical Synthesis and Analysis","score":0.840399980545044,"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":0.840399980545044,"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":0.03440000116825104,"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":0.022600000724196434,"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":0.6406999826431274},{"id":"https://openalex.org/keywords/categorical-variable","display_name":"Categorical variable","score":0.6121000051498413},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4860999882221222},{"id":"https://openalex.org/keywords/bayesian-network","display_name":"Bayesian network","score":0.4821000099182129},{"id":"https://openalex.org/keywords/flow","display_name":"Flow (mathematics)","score":0.4043000042438507},{"id":"https://openalex.org/keywords/bayes-theorem","display_name":"Bayes' theorem","score":0.367000013589859},{"id":"https://openalex.org/keywords/continuous-flow","display_name":"Continuous flow","score":0.3537999987602234},{"id":"https://openalex.org/keywords/variable-order-bayesian-network","display_name":"Variable-order Bayesian network","score":0.3481000065803528},{"id":"https://openalex.org/keywords/continuous-variable","display_name":"Continuous variable","score":0.33329999446868896},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.3328999876976013}],"concepts":[{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.6406999826431274},{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.6121000051498413},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.538100004196167},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.48969998955726624},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4860999882221222},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.4821000099182129},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.4043000042438507},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39399999380111694},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.367000013589859},{"id":"https://openalex.org/C186060115","wikidata":"https://www.wikidata.org/wiki/Q30336093","display_name":"Biological system","level":1,"score":0.3587000072002411},{"id":"https://openalex.org/C2985264121","wikidata":"https://www.wikidata.org/wiki/Q216320","display_name":"Continuous flow","level":2,"score":0.3537999987602234},{"id":"https://openalex.org/C71983512","wikidata":"https://www.wikidata.org/wiki/Q7915687","display_name":"Variable-order Bayesian network","level":4,"score":0.3481000065803528},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3343999981880188},{"id":"https://openalex.org/C2983394010","wikidata":"https://www.wikidata.org/wiki/Q19885330","display_name":"Continuous variable","level":2,"score":0.33329999446868896},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.3328999876976013},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.3174000084400177},{"id":"https://openalex.org/C106487976","wikidata":"https://www.wikidata.org/wiki/Q685816","display_name":"Matrix (chemical analysis)","level":2,"score":0.31189998984336853},{"id":"https://openalex.org/C18653775","wikidata":"https://www.wikidata.org/wiki/Q1333358","display_name":"Joint probability distribution","level":2,"score":0.2996000051498413},{"id":"https://openalex.org/C10184394","wikidata":"https://www.wikidata.org/wiki/Q5165491","display_name":"Continuous modelling","level":2,"score":0.29840001463890076},{"id":"https://openalex.org/C71139939","wikidata":"https://www.wikidata.org/wiki/Q910194","display_name":"Modal","level":2,"score":0.29820001125335693},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.29249998927116394},{"id":"https://openalex.org/C73301696","wikidata":"https://www.wikidata.org/wiki/Q5469984","display_name":"Formalism (music)","level":3,"score":0.2858000099658966},{"id":"https://openalex.org/C61224824","wikidata":"https://www.wikidata.org/wiki/Q2260434","display_name":"Mixture model","level":2,"score":0.28029999136924744},{"id":"https://openalex.org/C162500139","wikidata":"https://www.wikidata.org/wiki/Q2835887","display_name":"Estimation of distribution algorithm","level":2,"score":0.27489998936653137},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.2728999853134155},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.2728999853134155},{"id":"https://openalex.org/C121864883","wikidata":"https://www.wikidata.org/wiki/Q677916","display_name":"Statistical physics","level":1,"score":0.27140000462532043},{"id":"https://openalex.org/C55689738","wikidata":"https://www.wikidata.org/wiki/Q15963867","display_name":"Discrete time and continuous time","level":2,"score":0.26809999346733093},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.26030001044273376},{"id":"https://openalex.org/C68022304","wikidata":"https://www.wikidata.org/wiki/Q842217","display_name":"Bayes estimator","level":3,"score":0.2542000114917755},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2533999979496002},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.2531999945640564},{"id":"https://openalex.org/C101112237","wikidata":"https://www.wikidata.org/wiki/Q4874481","display_name":"Bayesian statistics","level":4,"score":0.2517000138759613},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.25060001015663147}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1609/aaai.v40i29.39679","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i29.39679","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1609/aaai.v40i29.39679","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i29.39679","pdf_url":null,"source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Diffusion":[0],"and":[1,57,83,126,151,178,188],"flow":[2,97,143,156],"matching":[3],"models":[4,19,64,105,114],"have":[5],"recently":[6],"emerged":[7],"as":[8],"promising":[9],"approaches":[10],"for":[11,68,99],"peptide":[12,101,180,200],"binder":[13,189],"design.":[14,201],"Despite":[15],"their":[16,33,120],"progress,":[17],"these":[18,89,167],"still":[20],"face":[21],"two":[22],"major":[23],"challenges.":[24],"First,":[25],"categorical":[26],"sampling":[27],"of":[28,79,196],"discrete":[29,115],"residue":[30,116,160],"types":[31,117],"collapses":[32],"continuous":[34,40,110,121,132],"parameters":[35],"into":[36],"one-hot":[37],"assignments,":[38],"while":[39],"variables":[41],"(e.g.,":[42],"atom":[43],"positions)":[44],"evolve":[45],"smoothly":[46],"throughout":[47],"the":[48,54,75,94,146,163,193],"generation":[49],"process.":[50],"This":[51],"mismatch":[52],"disrupts":[53],"update":[55],"dynamics":[56],"results":[58],"in":[59,108,198],"suboptimal":[60],"performance.":[61],"Second,":[62],"current":[63],"assume":[65],"unimodal":[66],"distributions":[67,107,169],"side-chain":[69,80,148,184],"torsion":[70],"angles,":[71],"which":[72],"conflicts":[73],"with":[74,130],"inherently":[76],"multimodal":[77,147],"nature":[78],"rotameric":[81,149],"states":[82,150],"limits":[84],"prediction":[85],"accuracy.":[86],"To":[87],"address":[88],"limitations,":[90],"we":[91],"introduce":[92],"PepBFN,":[93],"first":[95],"Bayesian":[96,128,142,174],"network":[98],"full-atom":[100],"design":[102,190],"that":[103],"directly":[104,158],"parameter":[106,122,168],"fully":[109],"space.":[111],"Specifically,":[112],"PepBFN":[113,197],"by":[118],"learning":[119],"distributions,":[123],"enabling":[124],"joint":[125],"smooth":[127,177],"updates":[129],"other":[131],"structural":[133],"parameters.":[134],"It":[135],"further":[136],"employs":[137],"a":[138,152],"novel":[139],"Gaussian":[140],"mixture-based":[141],"to":[144,157],"capture":[145],"Matrix":[153],"Fisher-based":[154],"Riemannian":[155],"model":[159],"orientations":[161],"on":[162,183],"SO(3)":[164],"manifold.":[165],"Together,":[166],"are":[170],"progressively":[171],"refined":[172],"via":[173],"updates,":[175],"yielding":[176],"coherent":[179],"generation.":[181],"Experiments":[182],"packing,":[185],"reverse":[186],"folding,":[187],"tasks":[191],"demonstrate":[192],"strong":[194],"potential":[195],"computational":[199]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2026-03-18T00:00:00"}
