{"id":"https://openalex.org/W7138237425","doi":"https://doi.org/10.1609/aaai.v40i2.37105","title":"Informative Subgraph Extraction with Deep Reinforcement Learning for Drug-Drug Interaction Prediction","display_name":"Informative Subgraph Extraction with Deep Reinforcement Learning for Drug-Drug Interaction Prediction","publication_year":2026,"publication_date":"2026-03-14","ids":{"openalex":"https://openalex.org/W7138237425","doi":"https://doi.org/10.1609/aaai.v40i2.37105"},"language":null,"primary_location":{"id":"doi:10.1609/aaai.v40i2.37105","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i2.37105","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/37105/41067","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://ojs.aaai.org/index.php/AAAI/article/download/37105/41067","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5043459237","display_name":"Jiancong Xie","orcid":"https://orcid.org/0000-0001-9030-0709"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jiancong Xie","raw_affiliation_strings":["School of Computer Science and Engineering, Sun Yat-sen University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Sun Yat-sen University, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129738150","display_name":"Wentao Wei","orcid":null},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wentao Wei","raw_affiliation_strings":["School of Computer Science and Engineering, Sun Yat-sen University, China\nPengcheng Laboratory, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Sun Yat-sen University, China\nPengcheng Laboratory, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129677875","display_name":"Chi Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chi Zhang","raw_affiliation_strings":["School of Computer Science and Engineering, Sun Yat-sen University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Sun Yat-sen University, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015329746","display_name":"Jiahua Rao","orcid":"https://orcid.org/0000-0002-6840-8198"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiahua Rao","raw_affiliation_strings":["School of Computer Science and Engineering, Sun Yat-sen University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Sun Yat-sen University, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5129653107","display_name":"Yuedong Yang","orcid":null},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuedong Yang","raw_affiliation_strings":["School of Computer Science and Engineering, Sun Yat-sen University, China\nKey Laboratory of Machine Intelligence and Advanced Computing, Sun Yat-sen University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Sun Yat-sen University, China\nKey Laboratory of Machine Intelligence and Advanced Computing, Sun Yat-sen University, China","institution_ids":["https://openalex.org/I157773358"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5043459237"],"corresponding_institution_ids":["https://openalex.org/I157773358"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.38095238,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"40","issue":"2","first_page":"1319","last_page":"1327"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10211","display_name":"Computational Drug Discovery Methods","score":0.656000018119812,"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":0.656000018119812,"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":0.23360000550746918,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.04529999941587448,"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":0.8485000133514404},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.5575000047683716},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4948999881744385},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.4788999855518341},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.43939998745918274},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.4131999909877777},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.3587999939918518},{"id":"https://openalex.org/keywords/information-extraction","display_name":"Information extraction","score":0.3499000072479248},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.34929999709129333}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.8485000133514404},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7110999822616577},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5835000276565552},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.5575000047683716},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5453000068664551},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4948999881744385},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.4788999855518341},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.43939998745918274},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.4131999909877777},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.3587999939918518},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.3499000072479248},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.34929999709129333},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.3463999927043915},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.32350000739097595},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.31769999861717224},{"id":"https://openalex.org/C2987255567","wikidata":"https://www.wikidata.org/wiki/Q33002955","display_name":"Knowledge graph","level":2,"score":0.3075999915599823},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.3059999942779541},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.301800012588501},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.29490000009536743},{"id":"https://openalex.org/C38764148","wikidata":"https://www.wikidata.org/wiki/Q17098245","display_name":"Interaction information","level":2,"score":0.29339998960494995},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.27250000834465027},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2718000113964081},{"id":"https://openalex.org/C120567893","wikidata":"https://www.wikidata.org/wiki/Q1582085","display_name":"Knowledge extraction","level":2,"score":0.27160000801086426},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.26339998841285706},{"id":"https://openalex.org/C12426560","wikidata":"https://www.wikidata.org/wiki/Q189569","display_name":"Basis (linear algebra)","level":2,"score":0.2533000111579895},{"id":"https://openalex.org/C74187038","wikidata":"https://www.wikidata.org/wiki/Q1418791","display_name":"Drug discovery","level":2,"score":0.25209999084472656}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1609/aaai.v40i2.37105","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i2.37105","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/37105/41067","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1609/aaai.v40i2.37105","is_oa":true,"landing_page_url":"https://doi.org/10.1609/aaai.v40i2.37105","pdf_url":"https://ojs.aaai.org/index.php/AAAI/article/download/37105/41067","source":{"id":"https://openalex.org/S4210191458","display_name":"Proceedings of the AAAI Conference on Artificial Intelligence","issn_l":"2159-5399","issn":["2159-5399","2374-3468"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/P4310320058","host_organization_name":"Association for the Advancement of Artificial Intelligence","host_organization_lineage":["https://openalex.org/P4310320058"],"host_organization_lineage_names":["Association for the Advancement of Artificial Intelligence"],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the AAAI Conference on Artificial Intelligence","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.7208293080329895,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321543","display_name":"China Postdoctoral Science Foundation","ror":"https://ror.org/0426zh255"},{"id":"https://openalex.org/F4320337111","display_name":"Basic and Applied Basic Research Foundation of Guangdong Province","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7138237425.pdf","grobid_xml":"https://content.openalex.org/works/W7138237425.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Drug-drug":[0],"interaction":[1,123],"(DDI)":[2],"prediction":[3],"is":[4,162],"pivotal":[5],"for":[6,30,115,121,156],"drug":[7,158,186,247],"safety":[8],"and":[9,19,41,54,137,147,153,180,199,218],"clinical":[10],"decision-making.":[11],"Recently,":[12],"subgraph-based":[13],"methods":[14,37,78],"utilizing":[15],"knowledge":[16,102,178],"graphs":[17],"(KGs)":[18],"domain":[20],"information":[21,100],"have":[22],"achieved":[23],"promising":[24],"results":[25],"by":[26,51,164],"extracting":[27],"informative":[28,67,152],"subgraphs":[29,155,193,233],"DDI":[31,205],"prediction.":[32,124],"However,":[33],"existing":[34],"subgraph":[35,129],"extraction":[36,130],"are":[38,49,195],"typically":[39],"coarse-grained":[40],"nonspecific,":[42],"facing":[43],"two":[44],"key":[45],"limitations:":[46],"First,":[47],"they":[48],"constrained":[50],"the":[52,65,70,83,128,150,173,177,181,185,190,231,235,243],"vast":[53],"noisy":[55],"nature":[56],"of":[57,73,87,97,184,192,223,230,237,246],"real-world":[58],"KGs,":[59],"making":[60],"it":[61],"challenging":[62],"to":[63,81,89,145,225],"identify":[64],"most":[66,151],"substructures":[68],"from":[69,176],"massive":[71],"space":[72],"candidate":[74],"subgraphs.":[75],"Second,":[76],"current":[77],"often":[79],"fail":[80],"exploit":[82],"molecular":[84,98,182],"structural":[85],"specificity":[86],"drugs":[88],"selectively":[90],"extract":[91,149],"relevant":[92,198],"subgraphs,":[93],"lacking":[94],"effective":[95],"integration":[96],"structure":[99],"with":[101],"graph":[103,179],"context.":[104],"To":[105],"address":[106],"these":[107],"challenges,":[108],"we":[109],"propose":[110],"RISE-DDI,":[111],"a":[112,132,139,165],"novel":[113],"framework":[114],"Reinforced-based":[116],"Informative":[117],"Subgraph":[118],"Extraction":[119],"approach":[120],"drug-drug":[122],"Specifically,":[125],"RISE-DDI":[126],"formulates":[127],"as":[131],"Markov":[133],"Decision":[134],"Process":[135],"(MDP)":[136],"leverages":[138],"deep":[140],"reinforcement":[141],"learning":[142],"(RL)":[143],"agent":[144,161],"dynamically":[146],"adaptively":[148],"context-specific":[154],"each":[157],"pair.":[159],"The":[160],"guided":[163],"learnable":[166],"structure-aware":[167],"reward":[168],"model":[169],"that":[170,194,209],"considers":[171],"both":[172,196,216],"topological":[174],"context":[175],"features":[183],"pairs,":[187],"thereby":[188],"encouraging":[189],"selection":[191],"structurally":[197],"biologically":[200],"informative.":[201],"Extensive":[202],"experiments":[203],"on":[204],"benchmark":[206],"datasets":[207],"demonstrate":[208],"our":[210,238],"method":[211],"outperforms":[212],"state-of-the-art":[213],"baselines":[214],"in":[215],"transductive":[217],"inductive":[219],"scenarios,":[220],"achieving":[221],"improvements":[222],"up":[224],"20%.":[226],"Furthermore,":[227],"visualization":[228],"analyses":[229],"extracted":[232],"highlight":[234],"interpretability":[236],"model,":[239],"providing":[240],"insights":[241],"into":[242],"underlying":[244],"mechanisms":[245],"interactions.":[248]},"counts_by_year":[],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2026-03-18T00:00:00"}
