{"id":"https://openalex.org/W7126190777","doi":"https://doi.org/10.1021/acs.jcim.5c02901","title":"XRepDDA: An Interpretable Drug\u2013Disease Association Prediction Framework Leveraging Pretrained Chemical Language Models","display_name":"XRepDDA: An Interpretable Drug\u2013Disease Association Prediction Framework Leveraging Pretrained Chemical Language Models","publication_year":2026,"publication_date":"2026-01-30","ids":{"openalex":"https://openalex.org/W7126190777","doi":"https://doi.org/10.1021/acs.jcim.5c02901","pmid":"https://pubmed.ncbi.nlm.nih.gov/41617662"},"language":"en","primary_location":{"id":"doi:10.1021/acs.jcim.5c02901","is_oa":false,"landing_page_url":"https://doi.org/10.1021/acs.jcim.5c02901","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/A5124369898","display_name":"Chenyi Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210153682","display_name":"Intelligent Health (United Kingdom)","ror":"https://ror.org/0576zak10","country_code":"GB","type":"company","lineage":["https://openalex.org/I4210153682"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Chenyi Zhang","raw_affiliation_strings":["Jiangnan University and Engineering Research Center of Intelligent Technology for Healthcare, Ministry of Education","School of Artificial Intelligence and Computer Science"],"affiliations":[{"raw_affiliation_string":"Jiangnan University and Engineering Research Center of Intelligent Technology for Healthcare, Ministry of Education","institution_ids":["https://openalex.org/I4210153682"]},{"raw_affiliation_string":"School of Artificial Intelligence and Computer Science","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065888073","display_name":"Yun Zuo","orcid":"https://orcid.org/0009-0009-3877-8102"},"institutions":[{"id":"https://openalex.org/I4210153682","display_name":"Intelligent Health (United Kingdom)","ror":"https://ror.org/0576zak10","country_code":"GB","type":"company","lineage":["https://openalex.org/I4210153682"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Yun Zuo","raw_affiliation_strings":["Jiangnan University and Engineering Research Center of Intelligent Technology for Healthcare, Ministry of Education","School of Artificial Intelligence and Computer Science"],"affiliations":[{"raw_affiliation_string":"Jiangnan University and Engineering Research Center of Intelligent Technology for Healthcare, Ministry of Education","institution_ids":["https://openalex.org/I4210153682"]},{"raw_affiliation_string":"School of Artificial Intelligence and Computer Science","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5122859556","display_name":"Qiao Ning","orcid":null},"institutions":[{"id":"https://openalex.org/I4210153682","display_name":"Intelligent Health (United Kingdom)","ror":"https://ror.org/0576zak10","country_code":"GB","type":"company","lineage":["https://openalex.org/I4210153682"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Qiao Ning","raw_affiliation_strings":["Jiangnan University and Engineering Research Center of Intelligent Technology for Healthcare, Ministry of Education","School of Artificial Intelligence and Computer Science"],"affiliations":[{"raw_affiliation_string":"Jiangnan University and Engineering Research Center of Intelligent Technology for Healthcare, Ministry of Education","institution_ids":["https://openalex.org/I4210153682"]},{"raw_affiliation_string":"School of Artificial Intelligence and Computer Science","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124299749","display_name":"Sisi Yuan","orcid":null},"institutions":[{"id":"https://openalex.org/I102149020","display_name":"University of North Carolina at Charlotte","ror":"https://ror.org/04dawnj30","country_code":"US","type":"education","lineage":["https://openalex.org/I102149020"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sisi Yuan","raw_affiliation_strings":["Department of Bioinformatics and Genomics","University of North Carolina at Charlotte"],"affiliations":[{"raw_affiliation_string":"Department of Bioinformatics and Genomics","institution_ids":[]},{"raw_affiliation_string":"University of North Carolina at Charlotte","institution_ids":["https://openalex.org/I102149020"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124302002","display_name":"Zhaohong Deng","orcid":null},"institutions":[{"id":"https://openalex.org/I4210153682","display_name":"Intelligent Health (United Kingdom)","ror":"https://ror.org/0576zak10","country_code":"GB","type":"company","lineage":["https://openalex.org/I4210153682"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Zhaohong Deng","raw_affiliation_strings":["Jiangnan University and Engineering Research Center of Intelligent Technology for Healthcare, Ministry of Education","School of Artificial Intelligence and Computer Science"],"affiliations":[{"raw_affiliation_string":"Jiangnan University and Engineering Research Center of Intelligent Technology for Healthcare, Ministry of Education","institution_ids":["https://openalex.org/I4210153682"]},{"raw_affiliation_string":"School of Artificial Intelligence and Computer Science","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5124429122","display_name":"Hongwei Yin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hongwei Yin","raw_affiliation_strings":["Department of Oncology","the First Affiliated Hospital of Naval Military Medical University"],"affiliations":[{"raw_affiliation_string":"Department of Oncology","institution_ids":[]},{"raw_affiliation_string":"the First Affiliated Hospital of Naval Military Medical University","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5064932972","display_name":"Anjing Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Anjing Zhao","raw_affiliation_strings":["Department of Oncology","the First Affiliated Hospital of Naval Military Medical University"],"affiliations":[{"raw_affiliation_string":"Department of Oncology","institution_ids":[]},{"raw_affiliation_string":"the First Affiliated Hospital of Naval Military Medical University","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5065888073"],"corresponding_institution_ids":["https://openalex.org/I4210153682"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.4898957,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"66","issue":"4","first_page":"2328","last_page":"2344"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10211","display_name":"Computational Drug Discovery Methods","score":0.9018999934196472,"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":0.9018999934196472,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.04230000078678131,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.007799999788403511,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.7854999899864197},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.5559999942779541},{"id":"https://openalex.org/keywords/chemical-space","display_name":"Chemical space","score":0.4846000075340271},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4099999964237213},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.37630000710487366},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.3682999908924103},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.36719998717308044},{"id":"https://openalex.org/keywords/chembl","display_name":"chEMBL","score":0.350600004196167},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.34040001034736633},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.33880001306533813}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.7854999899864197},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7803000211715698},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6869999766349792},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6697999835014343},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.5559999942779541},{"id":"https://openalex.org/C99726746","wikidata":"https://www.wikidata.org/wiki/Q906396","display_name":"Chemical space","level":3,"score":0.4846000075340271},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4099999964237213},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.37630000710487366},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3682999908924103},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.36719998717308044},{"id":"https://openalex.org/C63222358","wikidata":"https://www.wikidata.org/wiki/Q6120337","display_name":"chEMBL","level":3,"score":0.350600004196167},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.34040001034736633},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.33880001306533813},{"id":"https://openalex.org/C45942800","wikidata":"https://www.wikidata.org/wiki/Q245652","display_name":"Ensemble learning","level":2,"score":0.33809998631477356},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.33649998903274536},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.33480000495910645},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3294000029563904},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.30550000071525574},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.29980000853538513},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.29840001463890076},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.29660001397132874},{"id":"https://openalex.org/C2780022179","wikidata":"https://www.wikidata.org/wiki/Q1986794","display_name":"Molecular graph","level":3,"score":0.29510000348091125},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.2921000123023987},{"id":"https://openalex.org/C68762167","wikidata":"https://www.wikidata.org/wiki/Q910164","display_name":"Cheminformatics","level":2,"score":0.2904999852180481},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.28940001130104065},{"id":"https://openalex.org/C56173144","wikidata":"https://www.wikidata.org/wiki/Q1539893","display_name":"Pharmacophore","level":2,"score":0.2883000075817108},{"id":"https://openalex.org/C27158222","wikidata":"https://www.wikidata.org/wiki/Q5532422","display_name":"Generalizability theory","level":2,"score":0.2851000130176544},{"id":"https://openalex.org/C103697762","wikidata":"https://www.wikidata.org/wiki/Q4112105","display_name":"Virtual screening","level":3,"score":0.2833000123500824},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2827000021934509},{"id":"https://openalex.org/C123860398","wikidata":"https://www.wikidata.org/wiki/Q6934605","display_name":"Multiclass classification","level":3,"score":0.2743000090122223},{"id":"https://openalex.org/C136536468","wikidata":"https://www.wikidata.org/wiki/Q1225894","display_name":"Undersampling","level":2,"score":0.27140000462532043},{"id":"https://openalex.org/C74197172","wikidata":"https://www.wikidata.org/wiki/Q1195339","display_name":"Directed acyclic graph","level":2,"score":0.26930001378059387},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2513999938964844}],"mesh":[{"descriptor_ui":"D000077321","descriptor_name":"Deep Learning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D004194","descriptor_name":"Disease","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008956","descriptor_name":"Models, Chemical","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D012660","descriptor_name":"Semantics","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D058492","descriptor_name":"Drug Repositioning","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true}],"locations_count":2,"locations":[{"id":"doi:10.1021/acs.jcim.5c02901","is_oa":false,"landing_page_url":"https://doi.org/10.1021/acs.jcim.5c02901","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:41617662","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/41617662","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":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.7400298714637756}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":41,"referenced_works":["https://openalex.org/W74439177","https://openalex.org/W2107686700","https://openalex.org/W2113072832","https://openalex.org/W2119412782","https://openalex.org/W2151357092","https://openalex.org/W2159887157","https://openalex.org/W2200017991","https://openalex.org/W2346950316","https://openalex.org/W2417990257","https://openalex.org/W2527917780","https://openalex.org/W2767891136","https://openalex.org/W2777416523","https://openalex.org/W2790385355","https://openalex.org/W2805643613","https://openalex.org/W2887766329","https://openalex.org/W2892573831","https://openalex.org/W2911535432","https://openalex.org/W2912204568","https://openalex.org/W2916047525","https://openalex.org/W2946099214","https://openalex.org/W2961050676","https://openalex.org/W2995866605","https://openalex.org/W3012959663","https://openalex.org/W3093030756","https://openalex.org/W4211219865","https://openalex.org/W4224220091","https://openalex.org/W4284892498","https://openalex.org/W4289656533","https://openalex.org/W4296777868","https://openalex.org/W4321015480","https://openalex.org/W4361984358","https://openalex.org/W4381432349","https://openalex.org/W4388979610","https://openalex.org/W4390331481","https://openalex.org/W4391582532","https://openalex.org/W4391655725","https://openalex.org/W4401730694","https://openalex.org/W4403028081","https://openalex.org/W4407316259","https://openalex.org/W4409796174","https://openalex.org/W4410536383"],"related_works":[],"abstract_inverted_index":{"Drug":[0],"repositioning":[1],"aims":[2],"to":[3,77,122,193,240],"identify":[4,241],"new":[5],"indications":[6],"for":[7,16,206],"existing":[8,31],"drugs,":[9],"offering":[10],"a":[11,104,117,153,222],"cost-effective":[12],"and":[13,45,54,82,125,182,188,195,209,236,244,256],"time-efficient":[14],"strategy":[15,121],"therapeutic":[17],"development.":[18],"Its":[19],"core":[20],"challenge":[21],"lies":[22],"in":[23],"accurately":[24],"predicting":[25],"potential":[26],"drug-disease":[27],"associations":[28],"(DDAs).":[29],"However,":[30],"computational":[32],"approaches":[33],"often":[34],"suffer":[35],"from":[36],"inadequate":[37],"drug":[38,85],"representation,":[39,86,103],"insufficient":[40],"modeling":[41],"of":[42,191,216,260],"disease":[43,102,208],"semantics,":[44],"imbalanced":[46],"data":[47,166],"distributions,":[48],"which":[49,151],"collectively":[50],"limit":[51],"predictive":[52,217],"accuracy":[53,81],"generalization":[55],"ability.":[56],"To":[57,130,219],"address":[58],"these":[59],"challenges,":[60],"we":[61,134],"propose":[62],"an":[63,147],"innovative":[64],"framework,":[65],"termed":[66],"XRepDDA,":[67],"that":[68,169],"integrates":[69],"multimodal":[70],"feature":[71,231],"representation":[72],"with":[73,116,233],"deep":[74,158,183],"metric":[75,159],"learning":[76,184],"improve":[78],"DDA":[79],"prediction":[80,142],"robustness.":[83],"For":[84,101],"the":[87,110,136,247,253,257,261],"SMI-TED":[88],"pretrained":[89],"chemical":[90,254],"language":[91],"model":[92],"encodes":[93],"SMILES":[94],"sequences":[95],"into":[96],"chemically":[97],"informative":[98],"molecular":[99,199,237],"embeddings.":[100],"hierarchical":[105,124],"semantic":[106,126],"graph":[107,119],"based":[108],"on":[109,162,202],"MeSH":[111],"ontology":[112],"is":[113,144,226],"constructed":[114],"together":[115],"semantic-enhanced":[118],"embedding":[120,155],"capture":[123],"relationships":[127],"among":[128],"diseases.":[129],"mitigate":[131],"class":[132],"imbalance,":[133],"applied":[135],"AllKNN":[137],"adaptive":[138],"undersampling":[139],"strategy.":[140],"The":[141],"module":[143],"built":[145],"upon":[146],"improved":[148],"ModernNCA":[149],"architecture,":[150],"learns":[152],"discriminative":[154],"space":[156],"through":[157],"learning.":[160],"Experiments":[161],"multiple":[163],"public":[164],"benchmark":[165],"sets":[167],"demonstrate":[168],"XRepDDA":[170],"consistently":[171],"outperforms":[172],"diverse":[173],"baseline":[174],"models,":[175],"including":[176],"traditional":[177],"machine":[178],"learning,":[179],"tree-based":[180],"ensemble,":[181],"methods,":[185],"achieving":[186],"AUC":[187],"AUPR":[189],"values":[190],"up":[192],"0.9990":[194],"0.9991,":[196],"respectively.":[197],"Furthermore,":[198],"docking":[200],"experiments":[201],"top-ranked":[203],"candidate":[204],"drugs":[205],"Alzheimer's":[207],"stomach":[210],"neoplasms":[211],"provide":[212],"<i>in":[213],"silico</i>":[214],"validation":[215],"reliability.":[218],"enhance":[220],"interpretability,":[221],"multilevel":[223],"explainability":[224],"framework":[225],"established,":[227],"combining":[228],"SHAP-based":[229],"global":[230],"attribution":[232],"attention":[234],"mechanisms":[235],"perturbation":[238],"analyses":[239],"key":[242],"features":[243],"pharmacophores":[245],"at":[246],"local":[248],"level.":[249],"These":[250],"results":[251],"support":[252],"interpretability":[255],"biological":[258],"plausibility":[259],"predictions.":[262]},"counts_by_year":[],"updated_date":"2026-02-24T06:16:03.338239","created_date":"2026-02-01T00:00:00"}
