{"id":"https://openalex.org/W7155053290","doi":"https://doi.org/10.48550/arxiv.2604.18467","title":"An Integrated Deep-Learning Framework for Peptide-Protein Interaction Prediction and Target-Conditioned Peptide Generation with ConGA-PepPI and TC-PepGen","display_name":"An Integrated Deep-Learning Framework for Peptide-Protein Interaction Prediction and Target-Conditioned Peptide Generation with ConGA-PepPI and TC-PepGen","publication_year":2026,"publication_date":"2026-04-20","ids":{"openalex":"https://openalex.org/W7155053290","doi":"https://doi.org/10.48550/arxiv.2604.18467"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.18467","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.18467","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":null,"license_id":null,"version":null,"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":"https://doi.org/10.48550/arxiv.2604.18467","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5134107821","display_name":"Chupei Tang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tang, Chupei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111348192","display_name":"Junxiao Kong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kong, Junxiao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134202279","display_name":"Moyu Tang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tang, Moyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134128865","display_name":"Di Wang","orcid":"https://orcid.org/0000-0002-0050-612X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Di","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124973001","display_name":"Jixiu Zhai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhai, Jixiu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134128436","display_name":"Ronghao Xie","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xie, Ronghao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5134185472","display_name":"Shangkun Sima","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sima, Shangkun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5027108468","display_name":"Tianchi Lu","orcid":"https://orcid.org/0000-0001-6111-2020"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lu, Tianchi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":8,"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/T12254","display_name":"Machine Learning in Bioinformatics","score":0.23119999468326569,"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/T12254","display_name":"Machine Learning in Bioinformatics","score":0.23119999468326569,"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/T12576","display_name":"vaccines and immunoinformatics approaches","score":0.19009999930858612,"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/T10044","display_name":"Protein Structure and Dynamics","score":0.12630000710487366,"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/peptide","display_name":"Peptide","score":0.7080000042915344},{"id":"https://openalex.org/keywords/template","display_name":"Template","score":0.4343000054359436},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.42149999737739563},{"id":"https://openalex.org/keywords/protein\u2013protein-interaction","display_name":"Protein\u2013protein interaction","score":0.4041999876499176},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.365200012922287},{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.362199991941452}],"concepts":[{"id":"https://openalex.org/C2779281246","wikidata":"https://www.wikidata.org/wiki/Q172847","display_name":"Peptide","level":2,"score":0.7080000042915344},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5615000128746033},{"id":"https://openalex.org/C70721500","wikidata":"https://www.wikidata.org/wiki/Q177005","display_name":"Computational biology","level":1,"score":0.4429999887943268},{"id":"https://openalex.org/C82714645","wikidata":"https://www.wikidata.org/wiki/Q438331","display_name":"Template","level":2,"score":0.4343000054359436},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.42149999737739563},{"id":"https://openalex.org/C11804247","wikidata":"https://www.wikidata.org/wiki/Q896177","display_name":"Protein\u2013protein interaction","level":2,"score":0.4041999876499176},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3889000117778778},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.365200012922287},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.36419999599456787},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.362199991941452},{"id":"https://openalex.org/C186060115","wikidata":"https://www.wikidata.org/wiki/Q30336093","display_name":"Biological system","level":1,"score":0.34610000252723694},{"id":"https://openalex.org/C2910669755","wikidata":"https://www.wikidata.org/wiki/Q172847","display_name":"Peptide mapping","level":4,"score":0.3343000113964081},{"id":"https://openalex.org/C2780841128","wikidata":"https://www.wikidata.org/wiki/Q5073781","display_name":"Characterization (materials science)","level":2,"score":0.3255000114440918},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.2953999936580658},{"id":"https://openalex.org/C167625842","wikidata":"https://www.wikidata.org/wiki/Q899763","display_name":"Peptide sequence","level":3,"score":0.28290000557899475}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.18467","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.18467","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.18467","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.18467","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"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":{"Motivation:":[0],"Peptide-protein":[1],"interactions":[2],"(PepPIs)":[3],"are":[4],"central":[5],"to":[6,78],"cellular":[7],"regulation":[8],"and":[9,36,56,72,99,112,118,140],"peptide":[10,29,49,116],"therapeutics,":[11],"but":[12],"experimental":[13],"characterization":[14],"remains":[15],"too":[16],"slow":[17],"for":[18,47],"large-scale":[19],"screening.":[20],"Existing":[21],"methods":[22],"usually":[23],"emphasize":[24],"either":[25],"interaction":[26],"prediction":[27,55,77],"or":[28],"generation,":[30],"leaving":[31],"candidate":[32],"prioritization,":[33],"residue-level":[34],"interpretation,":[35],"target-conditioned":[37,62,146],"expansion":[38],"insufficiently":[39],"integrated.":[40],"Results:":[41],"We":[42],"present":[43],"an":[44],"integrated":[45],"framework":[46],"early-stage":[48],"screening":[50],"that":[51],"combines":[52],"a":[53,61,125],"partner-aware":[54],"localization":[57],"model":[58,64],"(ConGA-PepPI)":[59],"with":[60,102],"generative":[63],"(TC-PepGen).":[65],"ConGA-PepPI":[66,95],"uses":[67],"asymmetric":[68],"encoding,":[69],"bidirectional":[70],"cross-attention,":[71],"progressive":[73],"transfer":[74],"from":[75],"pair":[76],"binding-site":[79,103],"localization,":[80],"while":[81],"TC-PepGen":[82,131],"preserves":[83],"target":[84],"information":[85],"throughout":[86],"autoregressive":[87],"decoding":[88],"via":[89],"layerwise":[90],"conditioning.":[91],"In":[92],"five-fold":[93],"cross-validation,":[94],"achieved":[96],"0.839":[97],"accuracy":[98],"0.921":[100],"AUROC,":[101],"AUPR":[104],"values":[105],"of":[106,130,145],"0.601":[107],"on":[108,114,121],"the":[109,115],"protein":[110],"side":[111],"0.950":[113],"side,":[117],"remained":[119],"competitive":[120],"external":[122],"benchmarks.":[123],"Under":[124],"controlled":[126],"length-conditioned":[127],"benchmark,":[128],"40.39%":[129],"peptides":[132],"exceeded":[133],"native":[134],"templates":[135],"in":[136],"AlphaFold":[137],"3":[138],"ipTM,":[139],"unconstrained":[141],"generation":[142],"retained":[143],"evidence":[144],"signal.":[147]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-04-22T00:00:00"}