{"id":"https://openalex.org/W7126082350","doi":"https://doi.org/10.1109/bibm66473.2025.11356204","title":"KGPrompt-DTA: Knowledge Graph Prompt-Enhanced Graph-Transformer for Drug-Target Binding Affinity Prediction","display_name":"KGPrompt-DTA: Knowledge Graph Prompt-Enhanced Graph-Transformer for Drug-Target Binding Affinity Prediction","publication_year":2025,"publication_date":"2025-12-15","ids":{"openalex":"https://openalex.org/W7126082350","doi":"https://doi.org/10.1109/bibm66473.2025.11356204"},"language":null,"primary_location":{"id":"doi:10.1109/bibm66473.2025.11356204","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11356204","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":"conference-paper","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/A5063135883","display_name":"Xingjian Han","orcid":"https://orcid.org/0000-0002-1899-1952"},"institutions":[{"id":"https://openalex.org/I185261750","display_name":"University of Toronto","ror":"https://ror.org/03dbr7087","country_code":"CA","type":"education","lineage":["https://openalex.org/I185261750"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Xingjian Han","raw_affiliation_strings":["Harvard University,Toronto,Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Harvard University,Toronto,Canada","institution_ids":["https://openalex.org/I185261750"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000091558","display_name":"Shuohua Zhou","orcid":null},"institutions":[{"id":"https://openalex.org/I183935753","display_name":"King's College London","ror":"https://ror.org/0220mzb33","country_code":"GB","type":"education","lineage":["https://openalex.org/I124357947","https://openalex.org/I183935753"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Shuohua Zhou","raw_affiliation_strings":["King&#x0027;s College London,London,United Kingdom"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"King&#x0027;s College London,London,United Kingdom","institution_ids":["https://openalex.org/I183935753"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035135319","display_name":"Ji Luo","orcid":"https://orcid.org/0000-0002-5805-4787"},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ji Luo","raw_affiliation_strings":["Tsinghua University,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Tsinghua University,Beijing,China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124175030","display_name":"Yang Li","orcid":null},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yang Li","raw_affiliation_strings":["University of Chinese Academy of Sciences,Beijing,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Chinese Academy of Sciences,Beijing,China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5124291669","display_name":"Jiacheng Shi","orcid":null},"institutions":[{"id":"https://openalex.org/I16285277","display_name":"William & Mary","ror":"https://ror.org/03hsf0573","country_code":"US","type":"education","lineage":["https://openalex.org/I16285277"]},{"id":"https://openalex.org/I267592682","display_name":"Williams (United States)","ror":"https://ror.org/007zhvp17","country_code":"US","type":"company","lineage":["https://openalex.org/I267592682"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jiacheng Shi","raw_affiliation_strings":["College of William &#x0026; Mary,Williamsburg,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of William &#x0026; Mary,Williamsburg,USA","institution_ids":["https://openalex.org/I16285277","https://openalex.org/I267592682"]}]}],"institutions":[],"countries_distinct_count":4,"institutions_distinct_count":6,"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":"01","last_page":"08"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10211","display_name":"Computational Drug Discovery Methods","score":0.888700008392334,"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"}},"topics":[{"id":"https://openalex.org/T10211","display_name":"Computational Drug Discovery Methods","score":0.888700008392334,"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"}},{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.03929999843239784,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10887","display_name":"Bioinformatics and Genomic Networks","score":0.01899999938905239,"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/benchmark","display_name":"Benchmark (surveying)","score":0.6330000162124634},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.618399977684021},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.5189999938011169},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5026000142097473},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4851999878883362},{"id":"https://openalex.org/keywords/domain-knowledge","display_name":"Domain knowledge","score":0.38580000400543213},{"id":"https://openalex.org/keywords/knowledge-representation-and-reasoning","display_name":"Knowledge representation and reasoning","score":0.35920000076293945}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7138000130653381},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6330000162124634},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.618399977684021},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5195000171661377},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.5189999938011169},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5026000142097473},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4851999878883362},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4758000075817108},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38999998569488525},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.38580000400543213},{"id":"https://openalex.org/C161301231","wikidata":"https://www.wikidata.org/wiki/Q3478658","display_name":"Knowledge representation and reasoning","level":2,"score":0.35920000076293945},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3391000032424927},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.33629998564720154},{"id":"https://openalex.org/C74187038","wikidata":"https://www.wikidata.org/wiki/Q1418791","display_name":"Drug discovery","level":2,"score":0.3203999996185303},{"id":"https://openalex.org/C120567893","wikidata":"https://www.wikidata.org/wiki/Q1582085","display_name":"Knowledge extraction","level":2,"score":0.3165000081062317},{"id":"https://openalex.org/C4554734","wikidata":"https://www.wikidata.org/wiki/Q593744","display_name":"Knowledge base","level":2,"score":0.287200003862381},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.2791000008583069},{"id":"https://openalex.org/C115925183","wikidata":"https://www.wikidata.org/wiki/Q1412694","display_name":"Knowledge-based systems","level":2,"score":0.2646999955177307},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.2526000142097473}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm66473.2025.11356204","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm66473.2025.11356204","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":0.5118924975395203,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":25,"referenced_works":["https://openalex.org/W2109991441","https://openalex.org/W2605952223","https://openalex.org/W2785947426","https://openalex.org/W3000369508","https://openalex.org/W3018980093","https://openalex.org/W3028589594","https://openalex.org/W3032123378","https://openalex.org/W3096561213","https://openalex.org/W4296311649","https://openalex.org/W4296890795","https://openalex.org/W4309452766","https://openalex.org/W4310295249","https://openalex.org/W4318391592","https://openalex.org/W4327550249","https://openalex.org/W4388452200","https://openalex.org/W4388455891","https://openalex.org/W4388615774","https://openalex.org/W4392454059","https://openalex.org/W4392564168","https://openalex.org/W4392913700","https://openalex.org/W4393479383","https://openalex.org/W4402741177","https://openalex.org/W4406895194","https://openalex.org/W4411555708","https://openalex.org/W4415538374"],"related_works":[],"abstract_inverted_index":{"Accurately":[0],"predicting":[1],"drug-target":[2,38],"binding":[3],"affinity":[4],"(DTA)":[5],"is":[6],"essential":[7],"for":[8],"accelerating":[9],"drug":[10],"discovery":[11],"and":[12,29,62,75,82,101,112,127,138],"reducing":[13],"experimental":[14],"costs.":[15],"Existing":[16],"deep":[17],"learning-based":[18],"DTA":[19],"methods":[20],"often":[21],"fail":[22],"to":[23,33,59,95],"effectively":[24],"integrate":[25],"heterogeneous":[26],"biological":[27],"knowledge":[28,51,73],"structural":[30,99],"information,":[31],"leading":[32],"suboptimal":[34],"generalization":[35],"across":[36],"diverse":[37],"pairs.":[39],"In":[40],"this":[41],"work,":[42],"we":[43,67],"propose":[44],"KGPrompt-DTA,":[45],"a":[46,55,86],"novel":[47],"framework":[48],"that":[49,115],"injects":[50],"graph-driven":[52],"prompts":[53,70,156],"into":[54],"hybrid":[56],"graph-transformer":[57,158],"architecture":[58],"enhance":[60],"molecular":[61,81],"protein":[63,83],"representation":[64],"learning.":[65,159],"Specifically,":[66],"construct":[68],"entity-relation-aware":[69],"from":[71],"biomedical":[72],"graphs":[74],"dynamically":[76],"fuse":[77],"them":[78],"with":[79,157],"graph-encoded":[80],"features":[84],"via":[85],"multilevel":[87],"attention":[88],"mechanism.":[89],"This":[90],"design":[91],"enables":[92],"the":[93,145,151],"model":[94],"capture":[96],"both":[97],"local":[98],"dependencies":[100],"high-level":[102],"semantic":[103],"associations.":[104],"Extensive":[105],"experiments":[106],"on":[107],"two":[108],"benchmark":[109],"datasets,":[110],"DAVIS":[111],"KIBA,":[113],"demonstrate":[114],"KGPrompt-DTA":[116],"consistently":[117],"outperforms":[118],"state-of-the-art":[119],"methods,":[120],"achieving":[121],"MSE":[122],"reductions":[123],"of":[124,147,153],"15.5":[125],"%":[126],"6.5":[128],"%,":[129],"respectively,":[130],"while":[131],"also":[132],"improving":[133],"CI,":[134],"<tex":[135],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[136],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">$r_{m}^{2}$</tex>,":[137],"Pearson":[139],"correlation":[140],"scores.":[141],"Ablation":[142],"studies":[143],"confirm":[144],"contribution":[146],"each":[148],"component,":[149],"highlighting":[150],"effectiveness":[152],"integrating":[154],"knowledge-informed":[155]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2026-01-30T00:00:00"}