{"id":"https://openalex.org/W7135213157","doi":"https://doi.org/10.48550/arxiv.2603.11125","title":"Co-Diffusion: An Affinity-Aware Two-Stage Latent Diffusion Framework for Generalizable Drug-Target Affinity Prediction","display_name":"Co-Diffusion: An Affinity-Aware Two-Stage Latent Diffusion Framework for Generalizable Drug-Target Affinity Prediction","publication_year":2026,"publication_date":"2026-03-11","ids":{"openalex":"https://openalex.org/W7135213157","doi":"https://doi.org/10.48550/arxiv.2603.11125"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.11125","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.11125","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.2603.11125","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5128989619","display_name":"Yining Qian","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Qian, Yining","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128943503","display_name":"Pengjie Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Pengjie","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5090660975","display_name":"Yixiao Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Yixiao","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5128974042","display_name":"An-Yang Lu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lu, An-Yang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129002182","display_name":"Cheng Tan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tan, Cheng","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129066528","display_name":"Shuang Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Shuang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5128960376","display_name":"Lijun Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Lijun","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5128989619"],"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":0.8040000200271606,"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":0.8040000200271606,"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":0.1031000018119812,"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/T11948","display_name":"Machine Learning in Materials Science","score":0.019200000911951065,"subfield":{"id":"https://openalex.org/subfields/2505","display_name":"Materials Chemistry"},"field":{"id":"https://openalex.org/fields/25","display_name":"Materials Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5049999952316284},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5004000067710876},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.47540000081062317},{"id":"https://openalex.org/keywords/latent-variable","display_name":"Latent variable","score":0.4706999957561493},{"id":"https://openalex.org/keywords/pharmacophore","display_name":"Pharmacophore","score":0.41780000925064087},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.37470000982284546},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.3675000071525574},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.35440000891685486},{"id":"https://openalex.org/keywords/chemical-space","display_name":"Chemical space","score":0.3456999957561493},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.3327000141143799}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5720999836921692},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5604000091552734},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5133000016212463},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5049999952316284},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5004000067710876},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.47540000081062317},{"id":"https://openalex.org/C51167844","wikidata":"https://www.wikidata.org/wiki/Q4422623","display_name":"Latent variable","level":2,"score":0.4706999957561493},{"id":"https://openalex.org/C56173144","wikidata":"https://www.wikidata.org/wiki/Q1539893","display_name":"Pharmacophore","level":2,"score":0.41780000925064087},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.37470000982284546},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.3675000071525574},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.35440000891685486},{"id":"https://openalex.org/C99726746","wikidata":"https://www.wikidata.org/wiki/Q906396","display_name":"Chemical space","level":3,"score":0.3456999957561493},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.3327000141143799},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.3287999927997589},{"id":"https://openalex.org/C103697762","wikidata":"https://www.wikidata.org/wiki/Q4112105","display_name":"Virtual screening","level":3,"score":0.32670000195503235},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.31769999861717224},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.30059999227523804},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.29409998655319214},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.29109999537467957},{"id":"https://openalex.org/C2989108626","wikidata":"https://www.wikidata.org/wiki/Q904407","display_name":"Drug target","level":2,"score":0.29019999504089355},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2872999906539917},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.28540000319480896},{"id":"https://openalex.org/C2775905019","wikidata":"https://www.wikidata.org/wiki/Q192572","display_name":"In silico","level":3,"score":0.2822999954223633},{"id":"https://openalex.org/C2779478453","wikidata":"https://www.wikidata.org/wiki/Q6889748","display_name":"Modularity (biology)","level":2,"score":0.27219998836517334},{"id":"https://openalex.org/C60908668","wikidata":"https://www.wikidata.org/wiki/Q690207","display_name":"Perceptron","level":3,"score":0.2653999924659729},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.2648000121116638},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.26429998874664307},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.26350000500679016},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2590000033378601},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.2549999952316284},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.25130000710487366},{"id":"https://openalex.org/C197115733","wikidata":"https://www.wikidata.org/wiki/Q1003136","display_name":"Forcing (mathematics)","level":2,"score":0.25049999356269836},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.25040000677108765}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.11125","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.11125","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.2603.11125","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.11125","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Predicting":[0],"drug-target":[1],"affinity":[2,114],"is":[3],"fundamental":[4],"to":[5,61,111],"virtual":[6],"screening":[7],"and":[8,29,38,79,151,175],"lead":[9],"optimization.":[10],"However,":[11],"existing":[12],"deep":[13],"models":[14],"often":[15],"suffer":[16],"from":[17,116],"representation":[18],"collapse":[19],"in":[20,128,183,187],"stringent":[21],"cold-start":[22],"regimes,":[23],"where":[24],"the":[25,33,89,93,109,124,143],"scarcity":[26],"of":[27,35,146],"labels":[28],"domain":[30],"shifts":[31],"prevent":[32],"learning":[34],"transferable":[36],"pharmacophores":[37],"binding":[39,95,152],"motifs.":[40],"In":[41],"this":[42],"paper,":[43],"we":[44,133],"propose":[45],"Co-Diffusion,":[46],"a":[47,56,66,104,138,179],"novel":[48,176],"affinity-aware":[49],"framework":[50],"that":[51,88,135,160],"redefines":[52],"DTA":[53,130],"prediction":[54],"as":[55,103],"constrained":[57],"latent":[58,74,90,101],"denoising":[59],"process":[60],"enhance":[62],"generalization.":[63],"Co-Diffusion":[64,136,161],"employs":[65],"two-stage":[67],"paradigm:":[68],"Stage":[69,97],"I":[70],"establishes":[71],"an":[72,83],"affinity-steered":[73],"manifold":[75],"by":[76],"aligning":[77],"drug":[78,147,185],"target":[80],"embeddings":[81],"under":[82],"explicit":[84],"supervised":[85],"objective,":[86],"ensuring":[87],"space":[91],"reflects":[92],"intrinsic":[94],"landscape.":[96],"II":[98],"introduces":[99],"modality-specific":[100],"diffusion":[102],"stochastic":[105],"perturb-and-denoise":[106],"regularizer,":[107],"forcing":[108],"model":[110],"recover":[112],"consistent":[113],"semantics":[115],"noisy":[117],"structural":[118],"representations.":[119],"This":[120],"approach":[121],"effectively":[122],"mitigates":[123],"reconstruction-regression":[125],"conflict":[126],"common":[127],"generative":[129],"models.":[131],"Theoretically,":[132],"show":[134],"maximizes":[137],"variational":[139],"lower":[140],"bound":[141],"on":[142,171],"joint":[144],"likelihood":[145],"structures,":[148],"protein":[149,177],"sequences,":[150],"strength.":[153],"Extensive":[154],"experiments":[155],"across":[156],"multiple":[157],"benchmarks":[158],"demonstrate":[159],"significantly":[162],"outperforms":[163],"state-of-the-art":[164],"baselines,":[165],"particularly":[166],"yielding":[167],"superior":[168],"zero-shot":[169],"generalization":[170],"unseen":[172],"molecular":[173],"scaffolds":[174],"families-paving":[178],"robust":[180],"path":[181],"for":[182],"silico":[184],"prioritization":[186],"unexplored":[188],"chemical":[189],"spaces.":[190]},"counts_by_year":[],"updated_date":"2026-03-14T06:46:50.379900","created_date":"2026-03-14T00:00:00"}
