{"id":"https://openalex.org/W4386764666","doi":"https://doi.org/10.1109/icbcb57893.2023.10246685","title":"CLDDI: A Novel Method for Predicting Drug-Drug Interaction Events Based on Graph Contrastive Learning","display_name":"CLDDI: A Novel Method for Predicting Drug-Drug Interaction Events Based on Graph Contrastive Learning","publication_year":2023,"publication_date":"2023-04-21","ids":{"openalex":"https://openalex.org/W4386764666","doi":"https://doi.org/10.1109/icbcb57893.2023.10246685"},"language":"en","primary_location":{"id":"doi:10.1109/icbcb57893.2023.10246685","doi":"https://doi.org/10.1109/icbcb57893.2023.10246685","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icbcb57893.2023.10246685","pdf_url":null,"source":{"id":"https://openalex.org/S7407087375","display_name":"International Conference on Bioinformatics and Computational Biology (ICBCB)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 11th International Conference on Bioinformatics and Computational Biology (ICBCB)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"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/A5050134236","display_name":"Rong Xu","orcid":"https://orcid.org/0000-0003-3127-4795","observed_orcids":["https://orcid.org/0000-0003-3127-4795"]},"institutions":[{"id":"https://openalex.org/I91935597","display_name":"University of South China","ror":"https://ror.org/03mqfn238","country_code":"CN","type":"education","lineage":["https://openalex.org/I91935597"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rong Xu","raw_affiliation_strings":["University of South China,School of Computer,Hengyang,China","School of Computer, University of South China, Hengyang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of South China,School of Computer,Hengyang,China","institution_ids":["https://openalex.org/I91935597"]},{"raw_affiliation_string":"School of Computer, University of South China, Hengyang, China","institution_ids":["https://openalex.org/I91935597"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066976445","display_name":"Lingyun Luo","orcid":"https://orcid.org/0000-0002-8239-9056","observed_orcids":["https://orcid.org/0000-0002-8239-9056"]},"institutions":[{"id":"https://openalex.org/I91935597","display_name":"University of South China","ror":"https://ror.org/03mqfn238","country_code":"CN","type":"education","lineage":["https://openalex.org/I91935597"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lingyun Luo","raw_affiliation_strings":["University of South China,School of Computer,Hengyang,China","School of Computer, University of South China, Hengyang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of South China,School of Computer,Hengyang,China","institution_ids":["https://openalex.org/I91935597"]},{"raw_affiliation_string":"School of Computer, University of South China, Hengyang, China","institution_ids":["https://openalex.org/I91935597"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100405109","display_name":"Zhiming Liu","orcid":"https://orcid.org/0000-0001-9771-3071","observed_orcids":["https://orcid.org/0000-0001-9771-3071","https://orcid.org/0000-0003-1465-9247"]},"institutions":[{"id":"https://openalex.org/I91935597","display_name":"University of South China","ror":"https://ror.org/03mqfn238","country_code":"CN","type":"education","lineage":["https://openalex.org/I91935597"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiming Liu","raw_affiliation_strings":["University of South China,School of Computer,Hengyang,China","School of Computer, University of South China, Hengyang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of South China,School of Computer,Hengyang,China","institution_ids":["https://openalex.org/I91935597"]},{"raw_affiliation_string":"School of Computer, University of South China, Hengyang, China","institution_ids":["https://openalex.org/I91935597"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112883084","display_name":"Chunping Ouyang","orcid":"https://orcid.org/0000-0002-2154-0079","observed_orcids":["https://orcid.org/0000-0002-2154-0079","https://orcid.org/0009-0008-7393-9226"]},"institutions":[{"id":"https://openalex.org/I91935597","display_name":"University of South China","ror":"https://ror.org/03mqfn238","country_code":"CN","type":"education","lineage":["https://openalex.org/I91935597"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chunping Ouyang","raw_affiliation_strings":["University of South China,School of Computer,Hengyang,China","School of Computer, University of South China, Hengyang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of South China,School of Computer,Hengyang,China","institution_ids":["https://openalex.org/I91935597"]},{"raw_affiliation_string":"School of Computer, University of South China, Hengyang, China","institution_ids":["https://openalex.org/I91935597"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5035494769","display_name":"Yaping Wan","orcid":"https://orcid.org/0000-0001-9215-5488","observed_orcids":["https://orcid.org/0000-0001-9215-5488","https://orcid.org/0009-0001-6274-7199"]},"institutions":[{"id":"https://openalex.org/I91935597","display_name":"University of South China","ror":"https://ror.org/03mqfn238","country_code":"CN","type":"education","lineage":["https://openalex.org/I91935597"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yaping Wan","raw_affiliation_strings":["University of South China,School of Computer,Hengyang,China","School of Computer, University of South China, Hengyang, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of South China,School of Computer,Hengyang,China","institution_ids":["https://openalex.org/I91935597"]},{"raw_affiliation_string":"School of Computer, University of South China, Hengyang, China","institution_ids":["https://openalex.org/I91935597"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I91935597"],"apc_list":null,"apc_paid":null,"fwci":2.0448,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.90186335,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"105","last_page":"112"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10211","display_name":"Computational Drug Discovery Methods","score":0.8241999745368958,"subfield":{"id":"https://openalex.org/subfields/3002","display_name":"Drug Discovery"},"field":{"id":"https://openalex.org/fields/30","display_name":"Pharmacology, Toxicology and Pharmaceutics"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T10211","display_name":"Computational Drug Discovery Methods","score":0.8241999745368958,"subfield":{"id":"https://openalex.org/subfields/3002","display_name":"Drug Discovery"},"field":{"id":"https://openalex.org/fields/30","display_name":"Pharmacology, Toxicology and Pharmaceutics"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.11879999935626984,"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/T11943","display_name":"Pharmacovigilance and Adverse Drug Reactions","score":0.01889999955892563,"subfield":{"id":"https://openalex.org/subfields/3005","display_name":"Toxicology"},"field":{"id":"https://openalex.org/fields/30","display_name":"Pharmacology, Toxicology and Pharmaceutics"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/drug-drug-interactions","display_name":"drug-drug interactions","score":0.8629999756813049},{"id":"https://openalex.org/keywords/adverse-drug-reactions","display_name":"adverse drug reactions","score":0.8069999814033508},{"id":"https://openalex.org/keywords/graph-contrastive-learning","display_name":"graph contrastive learning","score":0.9549999833106995},{"id":"https://openalex.org/keywords/knowledge-graphs","display_name":"knowledge graphs","score":0.7620000243186951},{"id":"https://openalex.org/keywords/drug-embeddings","display_name":"drug embeddings","score":0.7730000019073486},{"id":"https://openalex.org/keywords/node-representation-learning","display_name":"node representation learning","score":0.7279999852180481},{"id":"https://openalex.org/keywords/end-to-end-learning","display_name":"end-to-end learning","score":0.6579999923706055},{"id":"https://openalex.org/keywords/drug-combination-safety","display_name":"drug combination safety","score":0.453000009059906}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.7680614590644836},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7423460483551025},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5954879522323608},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.5513906478881836},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5396241545677185},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5295218825340271},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.4949302077293396},{"id":"https://openalex.org/C2780035454","wikidata":"https://www.wikidata.org/wiki/Q8386","display_name":"Drug","level":2,"score":0.44137412309646606},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3430730402469635},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.22926583886146545},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09366336464881897},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C118552586","wikidata":"https://www.wikidata.org/wiki/Q7867","display_name":"Psychiatry","level":1,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icbcb57893.2023.10246685","doi":"https://doi.org/10.1109/icbcb57893.2023.10246685","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icbcb57893.2023.10246685","pdf_url":null,"source":{"id":"https://openalex.org/S7407087375","display_name":"International Conference on Bioinformatics and Computational Biology (ICBCB)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 11th International Conference on Bioinformatics and Computational Biology (ICBCB)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3","score":0.7246000170707703}],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320322843","display_name":"Natural Science Foundation of\u00a0Hunan Province","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1533230146","https://openalex.org/W1564644678","https://openalex.org/W1971387816","https://openalex.org/W1988037271","https://openalex.org/W2074050752","https://openalex.org/W2096451472","https://openalex.org/W2145877930","https://openalex.org/W2591704587","https://openalex.org/W2594153126","https://openalex.org/W2597646117","https://openalex.org/W2604314403","https://openalex.org/W2767891136","https://openalex.org/W2786016794","https://openalex.org/W2799720196","https://openalex.org/W2802200505","https://openalex.org/W2907492528","https://openalex.org/W2964420626","https://openalex.org/W2964473213","https://openalex.org/W2997021962","https://openalex.org/W3005680577","https://openalex.org/W3024894285","https://openalex.org/W3030536242","https://openalex.org/W3033039844","https://openalex.org/W3088680691","https://openalex.org/W3089843526","https://openalex.org/W3094497296","https://openalex.org/W3102419180","https://openalex.org/W3131450843","https://openalex.org/W3157889929","https://openalex.org/W3208102118","https://openalex.org/W3209882608","https://openalex.org/W4220796987","https://openalex.org/W4246413330"],"related_works":["https://openalex.org/W4403277309","https://openalex.org/W4382240023","https://openalex.org/W7126018746","https://openalex.org/W4406262012","https://openalex.org/W4398767396","https://openalex.org/W4403598866","https://openalex.org/W7128512594","https://openalex.org/W7118844156","https://openalex.org/W4411019729","https://openalex.org/W4404566131"],"abstract_inverted_index":{"Adverse":[0],"drug-drug":[1],"interactions":[2],"(DDIs)":[3],"may":[4],"occur":[5],"when":[6],"drugs":[7],"are":[8,44],"combined":[9],"to":[10,75,135,174],"treat":[11],"complex":[12],"or":[13,63],"comorbid":[14],"diseases,":[15],"which":[16,72],"can":[17],"result":[18],"in":[19,142,161,181],"adverse":[20],"drug":[21,62,115,150],"events,":[22],"injury,":[23],"and":[24,78,109,130,156,171,215],"even":[25],"death.":[26],"Therefore,":[27],"accurate":[28],"prediction":[29],"of":[30,53,69,104,113,139,179,194],"potential":[31],"DDI":[32,48,94,164],"events":[33,49,95],"is":[34,196],"critical.":[35],"Recently,":[36],"automated":[37],"computational":[38],"methods":[39,55],"such":[40],"as":[41],"deep":[42],"learning":[43,89,155],"widely":[45],"used":[46],"for":[47,91],"prediction.":[50],"However,":[51],"most":[52],"these":[54,143],"only":[56],"consider":[57],"single":[58],"information":[59,103,112,160],"about":[60],"the":[61,100,105,110,114,127,137,149,162,169,176,192,199],"rely":[64],"on":[65,187,204],"a":[66,84,132],"large":[67],"amount":[68],"label":[70],"data,":[71],"easily":[73],"leads":[74],"insufficient":[76],"robustness":[77],"generalization":[79,213],"ability.":[80],"Accordingly,":[81],"we":[82,118,147,167],"proposed":[83],"novel":[85],"end-to-end":[86,183],"graph":[87,122],"contrastive":[88,133,154,170],"model":[90],"predicting":[92],"multi-relational":[93,163],"(CLDDI).":[96],"It":[97],"comprehensively":[98],"considers":[99],"rich":[101],"biomedical":[102],"Knowledge":[106],"Graph":[107],"(KG)":[108],"structural":[111],"network.":[116,165],"Specifically,":[117],"first":[119],"generate":[120],"two":[121,144],"views":[123],"by":[124,153],"randomly":[125],"corrupting":[126],"original":[128],"KG,":[129],"compute":[131],"loss":[134,173],"maximize":[136],"agreement":[138],"node":[140],"representation":[141,178],"views.":[145],"Then":[146],"extract":[148],"embeddings":[151],"obtained":[152],"aggregate":[157],"their":[158],"neighbor":[159],"Finally,":[166],"combine":[168],"supervised":[172],"learn":[175],"feature":[177],"nodes":[180],"an":[182],"fashion.":[184],"Extensive":[185],"experiments":[186],"real":[188],"datasets":[189,206],"show":[190],"that":[191,209],"performance":[193,214],"CLDDI":[195,210],"competitive":[197],"with":[198],"best":[200],"baselines.":[201],"Experimental":[202],"results":[203],"sparse":[205],"further":[207],"demonstrate":[208],"has":[211],"strong":[212],"robustness.":[216]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":1}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2023-09-16T00:00:00"}