{"id":"https://openalex.org/W7126037960","doi":"https://doi.org/10.1109/bibm66473.2025.11356540","title":"Modeling TCR-pMHC Binding with Dual Encoders and Cross-Attention Fusion","display_name":"Modeling TCR-pMHC Binding with Dual Encoders and Cross-Attention Fusion","publication_year":2025,"publication_date":"2025-12-15","ids":{"openalex":"https://openalex.org/W7126037960","doi":"https://doi.org/10.1109/bibm66473.2025.11356540"},"language":null,"primary_location":{"id":"doi:10.1109/bibm66473.2025.11356540","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11356540","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/A5100350683","display_name":"Wenbo Wang","orcid":"https://orcid.org/0000-0003-4311-2646"},"institutions":[{"id":"https://openalex.org/I188592606","display_name":"Hamilton College","ror":"https://ror.org/05709zb94","country_code":"US","type":"education","lineage":["https://openalex.org/I188592606"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Wenbo Wang","raw_affiliation_strings":["Hamilton College,Computer Science,Clinton,USA"],"affiliations":[{"raw_affiliation_string":"Hamilton College,Computer Science,Clinton,USA","institution_ids":["https://openalex.org/I188592606"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124268110","display_name":"Cong Qi","orcid":null},"institutions":[{"id":"https://openalex.org/I118118575","display_name":"New Jersey Institute of Technology","ror":"https://ror.org/05e74xb87","country_code":"US","type":"education","lineage":["https://openalex.org/I118118575"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Cong Qi","raw_affiliation_strings":["New Jersey Institute of Technology,Computer Science,Newark,USA"],"affiliations":[{"raw_affiliation_string":"New Jersey Institute of Technology,Computer Science,Newark,USA","institution_ids":["https://openalex.org/I118118575"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5122872114","display_name":"Zhi Wei","orcid":null},"institutions":[{"id":"https://openalex.org/I118118575","display_name":"New Jersey Institute of Technology","ror":"https://ror.org/05e74xb87","country_code":"US","type":"education","lineage":["https://openalex.org/I118118575"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhi Wei","raw_affiliation_strings":["New Jersey Institute of Technology,Computer Science,Newark,USA"],"affiliations":[{"raw_affiliation_string":"New Jersey Institute of Technology,Computer Science,Newark,USA","institution_ids":["https://openalex.org/I118118575"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100350683"],"corresponding_institution_ids":["https://openalex.org/I188592606"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.70180355,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"5083","last_page":"5090"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12576","display_name":"vaccines and immunoinformatics approaches","score":0.9951000213623047,"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":0.9951000213623047,"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/T10031","display_name":"T-cell and B-cell Immunology","score":0.0012000000569969416,"subfield":{"id":"https://openalex.org/subfields/2403","display_name":"Immunology"},"field":{"id":"https://openalex.org/fields/24","display_name":"Immunology and Microbiology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11016","display_name":"Monoclonal and Polyclonal Antibodies Research","score":0.000699999975040555,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5968000292778015},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.5849999785423279},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.4964999854564667},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.435699999332428},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.4336000084877014},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.428600013256073},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4104999899864197},{"id":"https://openalex.org/keywords/computational-model","display_name":"Computational model","score":0.35519999265670776},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.35179999470710754}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6306999921798706},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5968000292778015},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.5849999785423279},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5202999711036682},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.4964999854564667},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.435699999332428},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.4336000084877014},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.428600013256073},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4104999899864197},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3659000098705292},{"id":"https://openalex.org/C70721500","wikidata":"https://www.wikidata.org/wiki/Q177005","display_name":"Computational biology","level":1,"score":0.35839998722076416},{"id":"https://openalex.org/C66024118","wikidata":"https://www.wikidata.org/wiki/Q1122506","display_name":"Computational model","level":2,"score":0.35519999265670776},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.35179999470710754},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.33230000734329224},{"id":"https://openalex.org/C195616568","wikidata":"https://www.wikidata.org/wiki/Q128711","display_name":"Epitope","level":3,"score":0.33070001006126404},{"id":"https://openalex.org/C19317047","wikidata":"https://www.wikidata.org/wiki/Q412037","display_name":"T-cell receptor","level":4,"score":0.3287999927997589},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3260999917984009},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.31450000405311584},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.2888000011444092},{"id":"https://openalex.org/C2780598303","wikidata":"https://www.wikidata.org/wiki/Q65921492","display_name":"Flexibility (engineering)","level":2,"score":0.28110000491142273},{"id":"https://openalex.org/C47701112","wikidata":"https://www.wikidata.org/wiki/Q735188","display_name":"Protein structure","level":2,"score":0.2759000062942505},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.2757999897003174},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.2736999988555908},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.2718000113964081},{"id":"https://openalex.org/C45484198","wikidata":"https://www.wikidata.org/wiki/Q827246","display_name":"Sequence alignment","level":4,"score":0.26570001244544983},{"id":"https://openalex.org/C3018795828","wikidata":"https://www.wikidata.org/wiki/Q899107","display_name":"Binding affinities","level":3,"score":0.26429998874664307},{"id":"https://openalex.org/C171018156","wikidata":"https://www.wikidata.org/wiki/Q7370306","display_name":"Rotation formalisms in three dimensions","level":2,"score":0.2605000138282776},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.2542000114917755}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm66473.2025.11356540","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11356540","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":0.7269349098205566,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W1975147762","https://openalex.org/W2016899885","https://openalex.org/W2031011895","https://openalex.org/W2042964520","https://openalex.org/W2163660115","https://openalex.org/W2670890989","https://openalex.org/W2797967938","https://openalex.org/W2973114758","https://openalex.org/W3113525558","https://openalex.org/W3135351475","https://openalex.org/W3146944767","https://openalex.org/W3160420762","https://openalex.org/W3163602801","https://openalex.org/W3183450452","https://openalex.org/W3185560091","https://openalex.org/W3196225703","https://openalex.org/W3196626346","https://openalex.org/W3198942356","https://openalex.org/W3210165781","https://openalex.org/W4286631898","https://openalex.org/W4295540029","https://openalex.org/W4304014045","https://openalex.org/W4307166771","https://openalex.org/W4313485929","https://openalex.org/W4323974789","https://openalex.org/W4362521490","https://openalex.org/W4376646211","https://openalex.org/W4379780265","https://openalex.org/W4384071683","https://openalex.org/W4389504934","https://openalex.org/W4389639889","https://openalex.org/W4391224707","https://openalex.org/W4401481784"],"related_works":[],"abstract_inverted_index":{"Accurately":[0],"modeling":[1],"the":[2,23,30,112,159],"binding":[3,155],"between":[4],"T-cell":[5],"receptors":[6],"(TCRs)":[7],"and":[8,18,29,54,64,84,91,98,118,126,136,157],"peptide-MHC":[9],"(pMHC)":[10],"complexes":[11],"is":[12],"essential":[13],"for":[14,153,161],"guiding":[15],"immunotherapy":[16],"development":[17],"personalized":[19],"vaccine":[20],"design.":[21],"However,":[22],"vast":[24],"diversity":[25],"of":[26,32,62],"TCR":[27,68],"repertoires":[28],"scarcity":[31],"experimentally":[33],"validated":[34],"interactions":[35],"make":[36],"generalization":[37],"to":[38,58,88,129],"unseen":[39],"epitopes":[40],"challenging.":[41],"This":[42],"paper":[43],"proposes":[44],"TIDE,":[45,67],"a":[46,150],"cross-attention-driven":[47],"dual-encoder":[48],"framework":[49],"that":[50,141],"leverages":[51],"large":[52],"protein":[53],"molecular":[55],"language":[56,144],"models":[57,145],"learn":[59],"discriminative":[60],"representations":[61],"TCRs":[63],"peptides.":[65],"In":[66],"sequences":[69],"are":[70,79],"encoded":[71],"using":[72],"Evolutionary":[73],"Scale":[74],"Modeling":[75],"(ESM),":[76],"while":[77],"peptides":[78],"transformed":[80],"into":[81],"SMILES":[82],"strings":[83],"processed":[85],"by":[86],"MolFormer":[87],"capture":[89],"chemical":[90],"structural":[92,108],"properties.":[93],"Multi-layer":[94],"cross-attention":[95,147],"then":[96],"refines":[97],"integrates":[99],"these":[100],"embeddings,":[101],"highlighting":[102],"interaction-relevant":[103],"patterns":[104],"without":[105],"requiring":[106],"explicit":[107],"alignment.":[109],"Evaluated":[110],"on":[111],"TCHard":[113],"benchmark":[114],"under":[115],"both":[116],"zero-shot":[117],"few-shot":[119],"settings,":[120],"TIDE":[121],"achieves":[122],"superior":[123],"predictive":[124],"accuracy":[125],"robustness":[127],"compared":[128],"state-of-the-art":[130],"baselines":[131],"such":[132],"as":[133],"ChemBERTa,":[134],"TITAN,":[135],"NetTCR.":[137],"These":[138],"results":[139],"demonstrate":[140],"combining":[142],"pretrained":[143],"with":[146],"fusion":[148],"offers":[149],"powerful":[151],"approach":[152],"TCR-pMHC":[154],"prediction":[156],"paves":[158],"way":[160],"more":[162],"reliable":[163],"computational":[164],"immunology":[165],"applications.":[166]},"counts_by_year":[],"updated_date":"2026-02-23T20:09:44.859080","created_date":"2026-01-30T00:00:00"}
