{"id":"https://openalex.org/W7131305584","doi":"https://doi.org/10.1021/acs.jcim.5c02885","title":"PeptideNet: An Integrative Deep Learning Framework for Predicting Diverse Bioactive Peptides Using Protein Language Model Embeddings","display_name":"PeptideNet: An Integrative Deep Learning Framework for Predicting Diverse Bioactive Peptides Using Protein Language Model Embeddings","publication_year":2026,"publication_date":"2026-02-24","ids":{"openalex":"https://openalex.org/W7131305584","doi":"https://doi.org/10.1021/acs.jcim.5c02885","pmid":"https://pubmed.ncbi.nlm.nih.gov/41731742"},"language":"en","primary_location":{"id":"doi:10.1021/acs.jcim.5c02885","is_oa":false,"landing_page_url":"https://doi.org/10.1021/acs.jcim.5c02885","pdf_url":null,"source":{"id":"https://openalex.org/S167262187","display_name":"Journal of Chemical Information and Modeling","issn_l":"1549-9596","issn":["1549-9596","1549-960X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320006","host_organization_name":"American Chemical Society","host_organization_lineage":["https://openalex.org/P4310320006"],"host_organization_lineage_names":["American Chemical Society"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Chemical Information and Modeling","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","pubmed"],"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/A5081920024","display_name":"Hamza Zahid","orcid":"https://orcid.org/0009-0008-8877-5578"},"institutions":[{"id":"https://openalex.org/I80611190","display_name":"Jeonbuk National University","ror":"https://ror.org/05q92br09","country_code":"KR","type":"education","lineage":["https://openalex.org/I80611190"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Hamza Zahid","raw_affiliation_strings":["Department of Electronics and Information Engineering","Jeonbuk National University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronics and Information Engineering","institution_ids":[]},{"raw_affiliation_string":"Jeonbuk National University","institution_ids":["https://openalex.org/I80611190"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126706474","display_name":"Maryam","orcid":null},"institutions":[{"id":"https://openalex.org/I80611190","display_name":"Jeonbuk National University","ror":"https://ror.org/05q92br09","country_code":"KR","type":"education","lineage":["https://openalex.org/I80611190"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Maryam","raw_affiliation_strings":["Department of Electronics and Information Engineering","Jeonbuk National University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronics and Information Engineering","institution_ids":[]},{"raw_affiliation_string":"Jeonbuk National University","institution_ids":["https://openalex.org/I80611190"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031342322","display_name":"Kil To Chong","orcid":"https://orcid.org/0000-0002-1952-0001"},"institutions":[{"id":"https://openalex.org/I4210142655","display_name":"Jusung Engineering (South Korea)","ror":"https://ror.org/0493cs919","country_code":"KR","type":"company","lineage":["https://openalex.org/I4210142655"]},{"id":"https://openalex.org/I80611190","display_name":"Jeonbuk National University","ror":"https://ror.org/05q92br09","country_code":"KR","type":"education","lineage":["https://openalex.org/I80611190"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Kil To Chong","raw_affiliation_strings":["CEO, Juyoungbio, Corp","Jeonbuk National University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CEO, Juyoungbio, Corp","institution_ids":["https://openalex.org/I4210142655"]},{"raw_affiliation_string":"Jeonbuk National University","institution_ids":["https://openalex.org/I80611190"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008953921","display_name":"Hilal Tayara","orcid":"https://orcid.org/0000-0001-5678-3479"},"institutions":[{"id":"https://openalex.org/I80611190","display_name":"Jeonbuk National University","ror":"https://ror.org/05q92br09","country_code":"KR","type":"education","lineage":["https://openalex.org/I80611190"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Hilal Tayara","raw_affiliation_strings":["Jeonbuk National University","School of International Engineering and Science"],"raw_orcid":"https://orcid.org/0000-0001-5678-3479","affiliations":[{"raw_affiliation_string":"Jeonbuk National University","institution_ids":["https://openalex.org/I80611190"]},{"raw_affiliation_string":"School of International Engineering and Science","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5008953921","https://openalex.org/A5031342322"],"corresponding_institution_ids":["https://openalex.org/I4210142655","https://openalex.org/I80611190"],"apc_list":null,"apc_paid":null,"fwci":20.4097,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.98795401,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":"66","issue":"5","first_page":"2616","last_page":"2626"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11103","display_name":"Antimicrobial Peptides and Activities","score":0.8241999745368958,"subfield":{"id":"https://openalex.org/subfields/2404","display_name":"Microbiology"},"field":{"id":"https://openalex.org/fields/24","display_name":"Immunology and Microbiology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T11103","display_name":"Antimicrobial Peptides and Activities","score":0.8241999745368958,"subfield":{"id":"https://openalex.org/subfields/2404","display_name":"Microbiology"},"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/T12254","display_name":"Machine Learning in Bioinformatics","score":0.0869000032544136,"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":0.025800000876188278,"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/deep-learning","display_name":"Deep learning","score":0.6111999750137329},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.49869999289512634},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.45829999446868896},{"id":"https://openalex.org/keywords/peptide","display_name":"Peptide","score":0.4474000036716461},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4417000114917755},{"id":"https://openalex.org/keywords/protein-sequencing","display_name":"Protein sequencing","score":0.42820000648498535},{"id":"https://openalex.org/keywords/peptide-sequence","display_name":"Peptide sequence","score":0.3792000114917755},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.37049999833106995},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.3393000066280365}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6814000010490417},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6166999936103821},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6111999750137329},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.49869999289512634},{"id":"https://openalex.org/C70721500","wikidata":"https://www.wikidata.org/wiki/Q177005","display_name":"Computational biology","level":1,"score":0.47429999709129333},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.45829999446868896},{"id":"https://openalex.org/C2779281246","wikidata":"https://www.wikidata.org/wiki/Q172847","display_name":"Peptide","level":2,"score":0.4474000036716461},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4417000114917755},{"id":"https://openalex.org/C10010492","wikidata":"https://www.wikidata.org/wiki/Q3142557","display_name":"Protein sequencing","level":4,"score":0.42820000648498535},{"id":"https://openalex.org/C167625842","wikidata":"https://www.wikidata.org/wiki/Q899763","display_name":"Peptide sequence","level":3,"score":0.3792000114917755},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.37049999833106995},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35359999537467957},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3393000066280365},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.32499998807907104},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.3249000012874603},{"id":"https://openalex.org/C132677234","wikidata":"https://www.wikidata.org/wiki/Q3273544","display_name":"Structural motif","level":2,"score":0.31949999928474426},{"id":"https://openalex.org/C66024118","wikidata":"https://www.wikidata.org/wiki/Q1122506","display_name":"Computational model","level":2,"score":0.31369999051094055},{"id":"https://openalex.org/C2994376932","wikidata":"https://www.wikidata.org/wiki/Q735188","display_name":"Amino acid residue","level":4,"score":0.3093999922275543},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.3073999881744385},{"id":"https://openalex.org/C35639132","wikidata":"https://www.wikidata.org/wiki/Q7452468","display_name":"Sequence labeling","level":3,"score":0.3018999993801117},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.30140000581741333},{"id":"https://openalex.org/C11804247","wikidata":"https://www.wikidata.org/wiki/Q896177","display_name":"Protein\u2013protein interaction","level":2,"score":0.2971000075340271},{"id":"https://openalex.org/C18051474","wikidata":"https://www.wikidata.org/wiki/Q899656","display_name":"Protein structure prediction","level":3,"score":0.295199990272522},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.26899999380111694},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.26759999990463257}],"mesh":[{"descriptor_ui":"D000077321","descriptor_name":"Deep Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000077321","descriptor_name":"Deep Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000077321","descriptor_name":"Deep Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D000098412","descriptor_name":"Predictive Learning Models","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000098415","descriptor_name":"Convolutional Neural Networks","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000098437","descriptor_name":"Prediction Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D000595","descriptor_name":"Amino Acid Sequence","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D010455","descriptor_name":"Peptides","qualifier_ui":"Q000494","qualifier_name":"pharmacology","is_major_topic":true},{"descriptor_ui":"D010455","descriptor_name":"Peptides","qualifier_ui":"Q000494","qualifier_name":"pharmacology","is_major_topic":true},{"descriptor_ui":"D010455","descriptor_name":"Peptides","qualifier_ui":"Q000494","qualifier_name":"pharmacology","is_major_topic":true},{"descriptor_ui":"D010455","descriptor_name":"Peptides","qualifier_ui":"Q000737","qualifier_name":"chemistry","is_major_topic":true},{"descriptor_ui":"D010455","descriptor_name":"Peptides","qualifier_ui":"Q000737","qualifier_name":"chemistry","is_major_topic":true},{"descriptor_ui":"D010455","descriptor_name":"Peptides","qualifier_ui":"Q000737","qualifier_name":"chemistry","is_major_topic":true},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D016571","descriptor_name":"Neural Networks, Computer","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false}],"locations_count":2,"locations":[{"id":"doi:10.1021/acs.jcim.5c02885","is_oa":false,"landing_page_url":"https://doi.org/10.1021/acs.jcim.5c02885","pdf_url":null,"source":{"id":"https://openalex.org/S167262187","display_name":"Journal of Chemical Information and Modeling","issn_l":"1549-9596","issn":["1549-9596","1549-960X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320006","host_organization_name":"American Chemical Society","host_organization_lineage":["https://openalex.org/P4310320006"],"host_organization_lineage_names":["American Chemical Society"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of Chemical Information and Modeling","raw_type":"journal-article"},{"id":"pmid:41731742","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/41731742","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of chemical information and modeling","raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G4069871152","display_name":null,"funder_award_id":"2020R1A2C2005612","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"},{"id":"https://openalex.org/G4910795082","display_name":null,"funder_award_id":"2022R1G1A1004613","funder_id":"https://openalex.org/F4320322120","funder_display_name":"National Research Foundation of Korea"}],"funders":[{"id":"https://openalex.org/F4320322120","display_name":"National Research Foundation of Korea","ror":"https://ror.org/013aysd81"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":43,"referenced_works":["https://openalex.org/W1981312603","https://openalex.org/W2097834518","https://openalex.org/W2589065420","https://openalex.org/W2791848964","https://openalex.org/W2794956775","https://openalex.org/W2944959957","https://openalex.org/W2946321682","https://openalex.org/W2963923670","https://openalex.org/W2965857035","https://openalex.org/W3009548091","https://openalex.org/W3025501158","https://openalex.org/W3111392271","https://openalex.org/W3112842176","https://openalex.org/W3146944767","https://openalex.org/W3165216240","https://openalex.org/W3177500196","https://openalex.org/W3203053846","https://openalex.org/W3216625938","https://openalex.org/W4200199004","https://openalex.org/W4225008496","https://openalex.org/W4225373802","https://openalex.org/W4280489441","https://openalex.org/W4286500588","https://openalex.org/W4323033940","https://openalex.org/W4324030730","https://openalex.org/W4327550249","https://openalex.org/W4362677280","https://openalex.org/W4385997203","https://openalex.org/W4386151602","https://openalex.org/W4393374578","https://openalex.org/W4394602519","https://openalex.org/W4394819507","https://openalex.org/W4396663720","https://openalex.org/W4400110431","https://openalex.org/W4400179006","https://openalex.org/W4403299835","https://openalex.org/W4404228904","https://openalex.org/W4404238226","https://openalex.org/W4408744223","https://openalex.org/W4409202352","https://openalex.org/W4409885212","https://openalex.org/W4412094245","https://openalex.org/W4412423882"],"related_works":[],"abstract_inverted_index":{"Bioactive":[0],"peptides":[1,50],"are":[2],"multifunctional":[3],"biomolecules":[4],"composed":[5],"of":[6,27,38,48,71,109,152,176],"short":[7],"amino":[8],"acid":[9],"sequences":[10],"that":[11],"exhibit":[12],"diverse":[13],"biological":[14],"activities,":[15],"including":[16,56],"antioxidative,":[17,119],"antihemolytic,":[18],"anticell-penetrating,":[19],"antiviral,":[20,117],"and":[21,36,64,66,82,96,114,121,146,195,201],"antimicrobial":[22],"effects.":[23],"Accurate":[24],"computational":[25],"prediction":[26],"peptide":[28,141,159,172],"bioactivity":[29,206],"is":[30],"essential":[31],"for":[32,116,204],"accelerating":[33],"the":[34,126,130,183,187],"discovery":[35],"design":[37],"peptide-based":[39],"therapeutics.":[40],"In":[41],"this":[42],"study,":[43],"we":[44],"investigated":[45],"five":[46],"categories":[47],"bioactive":[49],"using":[51],"four":[52],"distinct":[53],"feature":[54,132],"representations,":[55],"large":[57,177],"protein":[58,178],"language":[59,179],"model":[60,102,180,188],"embeddings":[61,135,181],"(ESM1,":[62],"ESM2,":[63],"ProtBert)":[65],"physicochemical":[67],"descriptors.":[68],"A":[69],"total":[70],"20":[72],"hybrid":[73],"deep":[74],"learning":[75],"models":[76],"integrating":[77],"Convolutional":[78],"Neural":[79],"Networks":[80],"(CNNs)":[81],"Bidirectional":[83],"Gated":[84],"Recurrent":[85],"Units":[86],"(BiGRUs)":[87],"were":[88],"developed":[89],"to":[90,171,189],"capture":[91,190],"both":[92,191],"local":[93],"sequence":[94,163],"motifs":[95],"long-range":[97],"dependencies.":[98],"The":[99,174],"proposed":[100],"PeptideNet":[101,184],"achieved":[103],"robust":[104],"predictive":[105],"performance,":[106],"with":[107,182],"accuracies":[108],"0.93,":[110],"0.94,":[111],"0.84,":[112],"0.89,":[113],"0.87":[115],"antimicrobial,":[118],"anticellpenetrating,":[120],"antihemolytic":[122],"peptides,":[123],"respectively,":[124],"on":[125],"independent":[127],"dataset.":[128],"Among":[129],"evaluated":[131],"sets,":[133],"ESM-2":[134],"consistently":[136],"outperformed":[137],"others":[138],"across":[139,158],"all":[140],"types,":[142],"providing":[143],"rich":[144],"contextual":[145,193],"evolutionary":[147],"information.":[148],"Furthermore,":[149],"t-SNE":[150],"visualization":[151],"learned":[153],"representations":[154],"demonstrated":[155],"effective":[156],"generalization":[157],"classes,":[160],"while":[161],"positional":[162],"logo":[164],"analysis":[165],"revealed":[166],"conserved":[167],"residue":[168],"patterns":[169],"contributing":[170],"bioactivity.":[173],"integration":[175],"architecture":[185],"enables":[186],"global":[192],"information":[194],"residue-level":[196],"features,":[197],"establishing":[198],"a":[199],"generalized":[200],"interpretable":[202],"framework":[203],"multipeptide":[205],"prediction.":[207]},"counts_by_year":[{"year":2026,"cited_by_count":2}],"updated_date":"2026-06-13T07:54:00.901334","created_date":"2026-02-25T00:00:00"}