{"id":"https://openalex.org/W7137860418","doi":"https://doi.org/10.48550/arxiv.2603.14792","title":"LaPro-DTA: Latent Dual-View Drug Representations and Salient Protein Feature Extraction for Generalizable Drug--Target Affinity Prediction","display_name":"LaPro-DTA: Latent Dual-View Drug Representations and Salient Protein Feature Extraction for Generalizable Drug--Target Affinity Prediction","publication_year":2026,"publication_date":"2026-03-16","ids":{"openalex":"https://openalex.org/W7137860418","doi":"https://doi.org/10.48550/arxiv.2603.14792"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.14792","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.14792","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":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.14792","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5129727636","display_name":"Zihan Dun","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dun, Zihan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5123630859","display_name":"Liuyi Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Liuyi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129673323","display_name":"An-Yang Lu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lu, An-Yang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129732801","display_name":"Shuang Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Shuang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5129682053","display_name":"Yining Qian","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qian, Yining","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"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/T10211","display_name":"Computational Drug Discovery Methods","score":0.8651000261306763,"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":0.8651000261306763,"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/T10044","display_name":"Protein Structure and Dynamics","score":0.03629999980330467,"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/T12254","display_name":"Machine Learning in Bioinformatics","score":0.03310000151395798,"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/overfitting","display_name":"Overfitting","score":0.852400004863739},{"id":"https://openalex.org/keywords/salient","display_name":"Salient","score":0.6171000003814697},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5090000033378601},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.45489999651908875},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4334000051021576},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.41620001196861267},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4129999876022339},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4092999994754791},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.37549999356269836}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.852400004863739},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7287999987602234},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6769999861717224},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.6171000003814697},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5674999952316284},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5090000033378601},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.45489999651908875},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4334000051021576},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41620001196861267},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4129999876022339},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4092999994754791},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.37549999356269836},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35659998655319214},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.34049999713897705},{"id":"https://openalex.org/C2776482837","wikidata":"https://www.wikidata.org/wiki/Q3553958","display_name":"Multi-label classification","level":2,"score":0.33550000190734863},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.3269999921321869},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.2955000102519989},{"id":"https://openalex.org/C19768560","wikidata":"https://www.wikidata.org/wiki/Q320727","display_name":"Dependency (UML)","level":2,"score":0.2897999882698059},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.2863999903202057},{"id":"https://openalex.org/C2989108626","wikidata":"https://www.wikidata.org/wiki/Q904407","display_name":"Drug target","level":2,"score":0.28519999980926514},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2761000096797943},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.26010000705718994},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.25940001010894775},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.25859999656677246},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.25850000977516174},{"id":"https://openalex.org/C89611455","wikidata":"https://www.wikidata.org/wiki/Q6804646","display_name":"Mechanism (biology)","level":2,"score":0.25209999084472656},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.25209999084472656}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.14792","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.14792","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.14792","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.14792","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":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Drug--target":[0],"affinity":[1],"prediction":[2],"is":[3],"pivotal":[4],"for":[5],"accelerating":[6],"drug":[7,61],"discovery,":[8],"yet":[9],"existing":[10],"methods":[11],"suffer":[12],"from":[13,33],"significant":[14],"performance":[15],"degradation":[16],"in":[17,163],"realistic":[18],"cold-start":[19],"scenarios":[20],"(unseen":[21],"drugs/targets/pairs),":[22],"primarily":[23],"driven":[24],"by":[25],"overfitting":[26],"to":[27,46,69,80,92,138],"training":[28],"instances":[29],"and":[30,49,76,123],"information":[31,104],"loss":[32],"irrelevant":[34],"target":[35],"sequences.":[36],"In":[37],"this":[38],"paper,":[39],"we":[40,56,106],"propose":[41],"LaPro-DTA,":[42],"a":[43,58,77,108,129],"framework":[44],"designed":[45],"achieve":[47],"robust":[48],"generalizable":[50],"DTA":[51],"prediction.":[52],"To":[53,102],"tackle":[54],"overfitting,":[55],"devise":[57],"latent":[59],"dual-view":[60],"representation":[62],"mechanism.":[63],"It":[64],"synergizes":[65],"an":[66,155],"instance-level":[67],"view":[68,79],"capture":[70],"fine-grained":[71],"substructures":[72],"with":[73],"stochastic":[74],"perturbation":[75],"distribution-level":[78],"distill":[81],"generalized":[82],"chemical":[83],"scaffolds":[84],"via":[85],"semantic":[86],"remapping,":[87],"thereby":[88],"enforcing":[89],"the":[90,160,164],"model":[91,139],"learn":[93],"transferable":[94],"structural":[95],"rules":[96],"rather":[97],"than":[98],"memorizing":[99],"specific":[100],"samples.":[101],"mitigate":[103],"loss,":[105],"introduce":[107],"salient":[109],"protein":[110],"feature":[111],"extraction":[112],"strategy":[113],"using":[114],"pattern-aware":[115],"top-$k$":[116],"pooling,":[117],"which":[118],"effectively":[119],"filters":[120],"background":[121],"noise":[122],"isolates":[124],"high-response":[125],"bioactive":[126],"regions.":[127],"Furthermore,":[128],"cross-view":[130],"multi-head":[131],"attention":[132],"mechanism":[133],"fuses":[134],"these":[135],"purified":[136],"features":[137],"comprehensive":[140],"interactions.":[141],"Extensive":[142],"experiments":[143],"on":[144,159],"benchmark":[145],"datasets":[146],"demonstrate":[147],"that":[148],"LaPro-DTA":[149],"significantly":[150],"outperforms":[151],"state-of-the-art":[152],"methods,":[153],"achieving":[154],"8\\%":[156],"MSE":[157],"reduction":[158],"Davis":[161],"dataset":[162],"challenging":[165],"unseen-drug":[166],"setting,":[167],"while":[168],"offering":[169],"interpretable":[170],"insights":[171],"into":[172],"binding":[173],"mechanisms.":[174]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-03-18T00:00:00"}
