{"id":"https://openalex.org/W7126096258","doi":"https://doi.org/10.1109/bibm66473.2025.11356839","title":"MetaPrior: Meta-Learning Guided Prior Injection for Few-Shot Antibody Affinity Prediction","display_name":"MetaPrior: Meta-Learning Guided Prior Injection for Few-Shot Antibody Affinity Prediction","publication_year":2025,"publication_date":"2025-12-15","ids":{"openalex":"https://openalex.org/W7126096258","doi":"https://doi.org/10.1109/bibm66473.2025.11356839"},"language":null,"primary_location":{"id":"doi:10.1109/bibm66473.2025.11356839","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11356839","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5124153741","display_name":"JiaShu","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":true,"raw_author_name":"JiaShu","raw_affiliation_strings":["Shanghai Jiao Tong University,Computer Science and Engineering,Shanghai,China"],"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University,Computer Science and Engineering,Shanghai,China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124150081","display_name":"Tingyao Li","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":"Tingyao Li","raw_affiliation_strings":["Shanghai Jiao Tong University,Computer Science and Engineering,Shanghai,China"],"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University,Computer Science and Engineering,Shanghai,China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100316433","display_name":"Zheyuan Wang","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":"Zheyuan Wang","raw_affiliation_strings":["Shanghai Jiao Tong University,Computer Science and Engineering,Shanghai,China"],"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University,Computer Science and Engineering,Shanghai,China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052961032","display_name":"Dezhi Wu","orcid":"https://orcid.org/0000-0001-7152-3069"},"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":"Dezhi Wu","raw_affiliation_strings":["Shanghai Jiao Tong University,Computer Science and Engineering,Shanghai,China"],"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University,Computer Science and Engineering,Shanghai,China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025327117","display_name":"Yiming Qin","orcid":"https://orcid.org/0000-0002-6375-6054"},"institutions":[{"id":"https://openalex.org/I4210153550","display_name":"Beijing Tsinghua Chang Gung Hospital","ror":"https://ror.org/050nfgr37","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210153550"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yiming Qin","raw_affiliation_strings":["School of Clinical Medicine, Beijing Tsinghua Changgung Hospital, Tsinghua Medicine, Tsinghua University,Beijing,China"],"affiliations":[{"raw_affiliation_string":"School of Clinical Medicine, Beijing Tsinghua Changgung Hospital, Tsinghua Medicine, Tsinghua University,Beijing,China","institution_ids":["https://openalex.org/I4210153550"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020435071","display_name":"Yihao Song","orcid":"https://orcid.org/0000-0002-8946-878X"},"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":"Yihao Song","raw_affiliation_strings":["Shanghai Jiao Tong University,Computer Science and Engineering,Shanghai,China"],"affiliations":[{"raw_affiliation_string":"Shanghai Jiao Tong University,Computer Science and Engineering,Shanghai,China","institution_ids":["https://openalex.org/I183067930"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122080267","display_name":"Yikai Wu","orcid":null},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yikai Wu","raw_affiliation_strings":["Human Phenome Institute, Fudan University,Shanghai,China"],"affiliations":[{"raw_affiliation_string":"Human Phenome Institute, Fudan University,Shanghai,China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123719797","display_name":"Tobias Pl\u00f6tz","orcid":null},"institutions":[{"id":"https://openalex.org/I164928964","display_name":"Merck (Germany)","ror":"https://ror.org/04b2dty93","country_code":"DE","type":"company","lineage":["https://openalex.org/I164928964"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Tobias Pl\u00f6tz","raw_affiliation_strings":["AI&Quantum Lab, Merck KGaA,Darmstadt,Germany"],"affiliations":[{"raw_affiliation_string":"AI&Quantum Lab, Merck KGaA,Darmstadt,Germany","institution_ids":["https://openalex.org/I164928964"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5116594288","display_name":"Karin Hrovatin","orcid":null},"institutions":[{"id":"https://openalex.org/I164928964","display_name":"Merck (Germany)","ror":"https://ror.org/04b2dty93","country_code":"DE","type":"company","lineage":["https://openalex.org/I164928964"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Karin Hrovatin","raw_affiliation_strings":["Digital Chemistry, Merck KGaA,Darmstadt,Germany"],"affiliations":[{"raw_affiliation_string":"Digital Chemistry, Merck KGaA,Darmstadt,Germany","institution_ids":["https://openalex.org/I164928964"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5116594287","display_name":"Stephanie M. Linker","orcid":null},"institutions":[{"id":"https://openalex.org/I164928964","display_name":"Merck (Germany)","ror":"https://ror.org/04b2dty93","country_code":"DE","type":"company","lineage":["https://openalex.org/I164928964"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Stephanie M Linker","raw_affiliation_strings":["Digital Chemistry, Merck KGaA,Darmstadt,Germany"],"affiliations":[{"raw_affiliation_string":"Digital Chemistry, Merck KGaA,Darmstadt,Germany","institution_ids":["https://openalex.org/I164928964"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124210292","display_name":"Alexander Vincent Hopp","orcid":null},"institutions":[{"id":"https://openalex.org/I164928964","display_name":"Merck (Germany)","ror":"https://ror.org/04b2dty93","country_code":"DE","type":"company","lineage":["https://openalex.org/I164928964"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Alexander Vincent Hopp","raw_affiliation_strings":["AI&Quantum Lab, Merck KGaA,Darmstadt,Germany"],"affiliations":[{"raw_affiliation_string":"AI&Quantum Lab, Merck KGaA,Darmstadt,Germany","institution_ids":["https://openalex.org/I164928964"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012135043","display_name":"Mathias Winkel","orcid":"https://orcid.org/0000-0002-0345-9701"},"institutions":[{"id":"https://openalex.org/I164928964","display_name":"Merck (Germany)","ror":"https://ror.org/04b2dty93","country_code":"DE","type":"company","lineage":["https://openalex.org/I164928964"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Mathias Winkel","raw_affiliation_strings":["AI&Quantum Lab, Merck KGaA,Darmstadt,Germany"],"affiliations":[{"raw_affiliation_string":"AI&Quantum Lab, Merck KGaA,Darmstadt,Germany","institution_ids":["https://openalex.org/I164928964"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124199777","display_name":"Philipp H. P. Harbach","orcid":null},"institutions":[{"id":"https://openalex.org/I164928964","display_name":"Merck (Germany)","ror":"https://ror.org/04b2dty93","country_code":"DE","type":"company","lineage":["https://openalex.org/I164928964"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Philipp H. P. Harbach","raw_affiliation_strings":["Merck KGaA,Darmstadt,Germany"],"affiliations":[{"raw_affiliation_string":"Merck KGaA,Darmstadt,Germany","institution_ids":["https://openalex.org/I164928964"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5052221471","display_name":"Feng Tan","orcid":"https://orcid.org/0009-0001-4754-4641"},"institutions":[{"id":"https://openalex.org/I164928964","display_name":"Merck (Germany)","ror":"https://ror.org/04b2dty93","country_code":"DE","type":"company","lineage":["https://openalex.org/I164928964"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Feng Tan","raw_affiliation_strings":["AI&Quantum Lab, Merck KGaA,Darmstadt,Germany"],"affiliations":[{"raw_affiliation_string":"AI&Quantum Lab, Merck KGaA,Darmstadt,Germany","institution_ids":["https://openalex.org/I164928964"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":14,"corresponding_author_ids":["https://openalex.org/A5124153741"],"corresponding_institution_ids":["https://openalex.org/I183067930"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.67265118,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1813","last_page":"1818"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12576","display_name":"vaccines and immunoinformatics approaches","score":0.5874000191688538,"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/T12576","display_name":"vaccines and immunoinformatics approaches","score":0.5874000191688538,"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":0.3215999901294708,"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/T10211","display_name":"Computational Drug Discovery Methods","score":0.01810000091791153,"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/generalizability-theory","display_name":"Generalizability theory","score":0.7071999907493591},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6486999988555908},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.5853000283241272},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.5403000116348267},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.3804999887943268},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.37400001287460327},{"id":"https://openalex.org/keywords/reduction","display_name":"Reduction (mathematics)","score":0.3610000014305115},{"id":"https://openalex.org/keywords/co-training","display_name":"Co-training","score":0.33970001339912415}],"concepts":[{"id":"https://openalex.org/C27158222","wikidata":"https://www.wikidata.org/wiki/Q5532422","display_name":"Generalizability theory","level":2,"score":0.7071999907493591},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6486999988555908},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6273999810218811},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.5853000283241272},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.5403000116348267},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5332000255584717},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4262000024318695},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.3804999887943268},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.37400001287460327},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.3610000014305115},{"id":"https://openalex.org/C2776959682","wikidata":"https://www.wikidata.org/wiki/Q17005296","display_name":"Co-training","level":3,"score":0.33970001339912415},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32100000977516174},{"id":"https://openalex.org/C3309286","wikidata":"https://www.wikidata.org/wiki/Q4907693","display_name":"Bilevel optimization","level":3,"score":0.3197999894618988},{"id":"https://openalex.org/C107908354","wikidata":"https://www.wikidata.org/wiki/Q4781456","display_name":"Applicability domain","level":3,"score":0.31380000710487366},{"id":"https://openalex.org/C2780092901","wikidata":"https://www.wikidata.org/wiki/Q3433612","display_name":"Correlation coefficient","level":2,"score":0.30559998750686646},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.2913999855518341},{"id":"https://openalex.org/C2164484","wikidata":"https://www.wikidata.org/wiki/Q5170150","display_name":"Core (optical fiber)","level":2,"score":0.2897000014781952},{"id":"https://openalex.org/C22679943","wikidata":"https://www.wikidata.org/wiki/Q159375","display_name":"Standard deviation","level":2,"score":0.2896000146865845},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.2874999940395355},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.2736000120639801},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.26660001277923584},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.26489999890327454},{"id":"https://openalex.org/C110121322","wikidata":"https://www.wikidata.org/wiki/Q865811","display_name":"Distribution (mathematics)","level":2,"score":0.26010000705718994}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm66473.2025.11356839","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11356839","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5366285443305969,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W2108767516","https://openalex.org/W2143210482","https://openalex.org/W2570949719","https://openalex.org/W2969325782","https://openalex.org/W3110687497","https://openalex.org/W3130104363","https://openalex.org/W3144239152","https://openalex.org/W4307348807","https://openalex.org/W4367666062","https://openalex.org/W4390174538","https://openalex.org/W4394582099","https://openalex.org/W4396613334","https://openalex.org/W4402727891","https://openalex.org/W4412102685"],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"antibody":[1,228],"affinity":[2,79,211,229],"prediction":[3],"is":[4,52,61,88,121],"crucial":[5],"for":[6,70,226],"drug":[7],"discovery.":[8],"However,":[9],"it":[10],"remains":[11],"challenging":[12],"due":[13],"to":[14,41,66,105,172,188],"data":[15,109,114,220],"scarcity,":[16],"which":[17],"limits":[18],"the":[19,91,103,107,161,166,183,198,218],"performance":[20,93,154],"of":[21,50,74,113,160,202],"deep":[22],"learning":[23],"models":[24],"in":[25,140,193],"few-shot,":[26],"antigen-specific":[27,145],"scenarios.":[28,142],"To":[29],"address":[30],"this":[31],"challenge,":[32],"we":[33],"propose":[34],"MetaPrior,":[35],"a":[36,53,57,71,77,95,152,223],"novel":[37],"meta-learning":[38],"framework":[39],"designed":[40],"learn":[42],"robust":[43],"prior":[44],"knowledge":[45],"before":[46],"fine-tuning.":[47],"The":[48,86],"core":[49],"MetaPrior":[51,120,150,213],"teacher-student":[54],"mechanism":[55],"where":[56],"Meta":[58],"Network":[59],"(teacher)":[60],"trained":[62],"via":[63],"bilevel":[64],"optimization":[65],"generate":[67],"high-quality":[68],"labels":[69],"diverse":[72],"set":[73],"pseudo-samples":[75,104],"and":[76,97,131,133,177,200,210],"binding":[78],"predictor":[80],"(student)":[81],"learns":[82],"from":[83,170,186],"these":[84],"labels.":[85],"teacher":[87],"optimized":[89],"by":[90,181],"student's":[92],"on":[94],"small":[96],"experimentally":[98],"measured":[99],"guide":[100],"set,":[101],"enabling":[102],"approximate":[106],"true":[108,219],"distribution":[110],"under":[111],"conditions":[112],"scarcity.":[115],"Extensive":[116],"experiments":[117],"demonstrate":[118],"that":[119],"compatible":[122],"with":[123,157,217],"popular":[124],"network":[125],"architectures":[126],"(e.g.,":[127],"MLPs,":[128],"CNN":[129],"s":[130],"Transformers)":[132],"significantly":[134],"surpasses":[135],"standard":[136,184],"fine-tuning":[137],"strategies,":[138],"particularly":[139],"data-scarce":[141],"Across":[143],"several":[144],"datasets,":[146],"such":[147],"as":[148],"VEGF,":[149],"achieves":[151],"notable":[153],"gain":[155],"even":[156],"only":[158],"40%":[159],"available":[162],"training":[163,179],"data,":[164],"increasing":[165],"Pearson":[167],"correlation":[168],"coefficient":[169],"0.38":[171],"0.58":[173],"(a":[174,190],"53%":[175],"improvement)":[176],"enhancing":[178],"stability":[180],"reducing":[182],"deviation":[185],"0.42":[187],"0.19":[189],"54.8%":[191],"reduction":[192],"variability).":[194],"These":[195],"results":[196],"confirm":[197],"robustness":[199],"generalizability":[201],"our":[203],"proposed":[204],"method.":[205],"By":[206],"jointly":[207],"optimizing":[208],"pseudo-labels":[209],"learning,":[212],"aligns":[214],"synthetic":[215],"supervision":[216],"distribution,":[221],"offering":[222],"principled":[224],"solution":[225],"few-shot":[227],"prediction.":[230]},"counts_by_year":[],"updated_date":"2026-02-01T03:34:12.195049","created_date":"2026-01-30T00:00:00"}