{"id":"https://openalex.org/W4405522680","doi":"https://doi.org/10.1109/cipae64326.2024.00099","title":"Simple Graph Sampling-Based Meta-Learning for Molecular Property Prediction","display_name":"Simple Graph Sampling-Based Meta-Learning for Molecular Property Prediction","publication_year":2024,"publication_date":"2024-08-26","ids":{"openalex":"https://openalex.org/W4405522680","doi":"https://doi.org/10.1109/cipae64326.2024.00099"},"language":"en","primary_location":{"id":"doi:10.1109/cipae64326.2024.00099","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cipae64326.2024.00099","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Conference on Computers, Information Processing and Advanced Education (CIPAE)","raw_type":"proceedings-article"},"type":"article","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/A5115525446","display_name":"Wang Chushi","orcid":null},"institutions":[{"id":"https://openalex.org/I1297991670","display_name":"Southwest University of Science and Technology","ror":"https://ror.org/04d996474","country_code":"CN","type":"education","lineage":["https://openalex.org/I1297991670"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Wang Chushi","raw_affiliation_strings":["Southwest University of Science and Technology, SWUST,Department of Computer Science and Engineering,Mianyang,China"],"affiliations":[{"raw_affiliation_string":"Southwest University of Science and Technology, SWUST,Department of Computer Science and Engineering,Mianyang,China","institution_ids":["https://openalex.org/I1297991670"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115525447","display_name":"Jia Shiyu","orcid":null},"institutions":[{"id":"https://openalex.org/I1297991670","display_name":"Southwest University of Science and Technology","ror":"https://ror.org/04d996474","country_code":"CN","type":"education","lineage":["https://openalex.org/I1297991670"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jia Shiyu","raw_affiliation_strings":["Southwest University of Science and Technology, SWUST,Department of Nuclear Engineering,Mianyang,China"],"affiliations":[{"raw_affiliation_string":"Southwest University of Science and Technology, SWUST,Department of Nuclear Engineering,Mianyang,China","institution_ids":["https://openalex.org/I1297991670"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5115525448","display_name":"Ren Xinyue","orcid":null},"institutions":[{"id":"https://openalex.org/I165745306","display_name":"Southwest Petroleum University","ror":"https://ror.org/03h17x602","country_code":"CN","type":"education","lineage":["https://openalex.org/I165745306"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ren Xinyue","raw_affiliation_strings":["Southwest Petroleum University, SWPU,Department of Electrical Engineering,Nanchong,China"],"affiliations":[{"raw_affiliation_string":"Southwest Petroleum University, SWPU,Department of Electrical Engineering,Nanchong,China","institution_ids":["https://openalex.org/I165745306"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5115525446"],"corresponding_institution_ids":["https://openalex.org/I1297991670"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.28327656,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"513","last_page":"518"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10211","display_name":"Computational Drug Discovery Methods","score":0.9842000007629395,"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":0.9842000007629395,"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/T12327","display_name":"Various Chemistry Research Topics","score":0.9782000184059143,"subfield":{"id":"https://openalex.org/subfields/1606","display_name":"Physical and Theoretical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10908","display_name":"Analytical Chemistry and Chromatography","score":0.9509000182151794,"subfield":{"id":"https://openalex.org/subfields/1607","display_name":"Spectroscopy"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6695853471755981},{"id":"https://openalex.org/keywords/simple","display_name":"Simple (philosophy)","score":0.6369073390960693},{"id":"https://openalex.org/keywords/property","display_name":"Property (philosophy)","score":0.569430410861969},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4910111725330353},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.44685274362564087},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.41128936409950256},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.4039367437362671},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3828029930591583}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6695853471755981},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.6369073390960693},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.569430410861969},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4910111725330353},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.44685274362564087},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41128936409950256},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4039367437362671},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3828029930591583},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.0},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cipae64326.2024.00099","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cipae64326.2024.00099","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Conference on Computers, Information Processing and Advanced Education (CIPAE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W1888005072","https://openalex.org/W2565684601","https://openalex.org/W2804057010","https://openalex.org/W3042330800","https://openalex.org/W3211132972","https://openalex.org/W6717697761","https://openalex.org/W6736057607","https://openalex.org/W6779119530"],"related_works":["https://openalex.org/W1585007175","https://openalex.org/W2382521049","https://openalex.org/W2144385241","https://openalex.org/W4300101996","https://openalex.org/W2165950148","https://openalex.org/W4253593777","https://openalex.org/W2951497643","https://openalex.org/W4403053866","https://openalex.org/W2142393343","https://openalex.org/W2338854850"],"abstract_inverted_index":{"We":[0],"focuses":[1],"on":[2],"the":[3,12,41,88,99,115,140],"challenges":[4],"and":[5,32,45,65,101,139],"advances":[6],"in":[7,53,87,122],"molecular":[8,104,123],"property":[9,105,124],"prediction":[10,125],"within":[11],"field":[13],"of":[14,43,103,118,142],"drug":[15],"discovery,":[16],"particularly":[17],"when":[18],"addressing":[19],"small":[20],"sample":[21],"sizes.":[22],"In":[23],"seeking":[24],"to":[25,67,91,131],"enhance":[26],"predictive":[27],"accuracy":[28,102],"while":[29],"reducing":[30],"costs":[31],"time":[33],"expenditure,":[34],"novel":[35],"computational":[36],"approaches":[37],"are":[38],"explored.":[39],"Notably,":[40],"integration":[42],"meta-learning":[44],"contrastive":[46],"learning":[47],"has":[48],"demonstrated":[49],"potential,":[50],"as":[51,61],"evidenced":[52],"methods.":[54],"Despite":[55],"these":[56,93,133],"advancements,":[57],"persistent":[58],"issues":[59],"such":[60],"slow":[62],"model":[63,90],"training":[64],"sensitivity":[66],"data":[68],"noise":[69,83,120,144],"undermine":[70],"practical":[71],"applicability.":[72],"Our":[73,107],"research":[74,128],"introduces":[75],"a":[76],"new":[77],"optimization":[78,121],"strategy":[79],"that":[80],"incorporates":[81],"random":[82,119],"during":[84],"subgraph":[85],"sampling":[86],"GS-Meta":[89],"tackle":[92],"challenges.":[94],"The":[95],"approach":[96],"significantly":[97],"increases":[98],"robustness":[100],"predictions.":[106],"findings,":[108],"corroborated":[109],"by":[110],"robust":[111],"performance":[112],"metrics,":[113],"indicate":[114],"promising":[116],"potential":[117],"models.":[126],"Future":[127],"should":[129],"continue":[130],"explore":[132],"techniques'":[134],"generalizability":[135],"across":[136],"larger":[137],"datasets":[138],"impact":[141],"different":[143],"injection":[145],"strategies.":[146]},"counts_by_year":[],"updated_date":"2025-12-22T23:10:17.713674","created_date":"2025-10-10T00:00:00"}
