{"id":"https://openalex.org/W4416016990","doi":"https://doi.org/10.1145/3746252.3761236","title":"Towards Few-shot Chemical Reaction Outcome Prediction","display_name":"Towards Few-shot Chemical Reaction Outcome Prediction","publication_year":2025,"publication_date":"2025-11-07","ids":{"openalex":"https://openalex.org/W4416016990","doi":"https://doi.org/10.1145/3746252.3761236"},"language":null,"primary_location":{"id":"doi:10.1145/3746252.3761236","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3746252.3761236","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 34th ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3746252.3761236","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5052815357","display_name":"Yili Shen","orcid":"https://orcid.org/0000-0002-0963-0960"},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Yili Shen","raw_affiliation_strings":["Department of Computer Science and Engineering, University of Notre Dame, NOTRE DAME, IN, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, University of Notre Dame, NOTRE DAME, IN, USA","institution_ids":["https://openalex.org/I107639228"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057838053","display_name":"Yijun Tian","orcid":"https://orcid.org/0000-0003-2795-6080"},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yijun Tian","raw_affiliation_strings":["Department of Computer Science and Engineering, University of Notre Dame, South Bend, IN, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, University of Notre Dame, South Bend, IN, USA","institution_ids":["https://openalex.org/I107639228"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025372898","display_name":"Cheng\u2010Wei Ju","orcid":"https://orcid.org/0000-0002-2250-8548"},"institutions":[{"id":"https://openalex.org/I40347166","display_name":"University of Chicago","ror":"https://ror.org/024mw5h28","country_code":"US","type":"education","lineage":["https://openalex.org/I40347166"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Cheng-Wei Ju","raw_affiliation_strings":["Pritzker School of Molecular Engineering, The University of Chicago, CHICAGO, IL, USA"],"affiliations":[{"raw_affiliation_string":"Pritzker School of Molecular Engineering, The University of Chicago, CHICAGO, IL, USA","institution_ids":["https://openalex.org/I40347166"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029114040","display_name":"Olaf Wiest","orcid":"https://orcid.org/0000-0001-9316-7720"},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Olaf Wiest","raw_affiliation_strings":["Department of Chemistry and Biochemistry, University of Notre Dame, NOTRE DAME, IN, USA"],"affiliations":[{"raw_affiliation_string":"Department of Chemistry and Biochemistry, University of Notre Dame, NOTRE DAME, IN, USA","institution_ids":["https://openalex.org/I107639228"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5000755750","display_name":"Xiangliang Zhang","orcid":"https://orcid.org/0000-0002-3574-5665"},"institutions":[{"id":"https://openalex.org/I107639228","display_name":"University of Notre Dame","ror":"https://ror.org/00mkhxb43","country_code":"US","type":"education","lineage":["https://openalex.org/I107639228"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiangliang Zhang","raw_affiliation_strings":["Department of Computer Science and Engineering, University of Notre Dame, NOTRE DAME, IN, USA"],"affiliations":[{"raw_affiliation_string":"Department of Computer Science and Engineering, University of Notre Dame, NOTRE DAME, IN, USA","institution_ids":["https://openalex.org/I107639228"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5052815357"],"corresponding_institution_ids":["https://openalex.org/I107639228"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.28647092,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2599","last_page":"2609"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.7523000240325928,"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"}},"topics":[{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.7523000240325928,"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"}},{"id":"https://openalex.org/T10211","display_name":"Computational Drug Discovery Methods","score":0.20430000126361847,"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":0.0052999998442828655,"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/task","display_name":"Task (project management)","score":0.5730999708175659},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5206999778747559},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4934999942779541},{"id":"https://openalex.org/keywords/outcome","display_name":"Outcome (game theory)","score":0.397599995136261},{"id":"https://openalex.org/keywords/adaptation","display_name":"Adaptation (eye)","score":0.3562999963760376},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.35499998927116394},{"id":"https://openalex.org/keywords/fingerprint","display_name":"Fingerprint (computing)","score":0.3418000042438507},{"id":"https://openalex.org/keywords/drug-discovery","display_name":"Drug discovery","score":0.3386000096797943}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6550999879837036},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5730999708175659},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5415999889373779},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5206999778747559},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4934999942779541},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4551999866962433},{"id":"https://openalex.org/C148220186","wikidata":"https://www.wikidata.org/wiki/Q7111912","display_name":"Outcome (game theory)","level":2,"score":0.397599995136261},{"id":"https://openalex.org/C139807058","wikidata":"https://www.wikidata.org/wiki/Q352374","display_name":"Adaptation (eye)","level":2,"score":0.3562999963760376},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.35499998927116394},{"id":"https://openalex.org/C2777826928","wikidata":"https://www.wikidata.org/wiki/Q3745713","display_name":"Fingerprint (computing)","level":2,"score":0.3418000042438507},{"id":"https://openalex.org/C74187038","wikidata":"https://www.wikidata.org/wiki/Q1418791","display_name":"Drug discovery","level":2,"score":0.3386000096797943},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.3246000111103058},{"id":"https://openalex.org/C68762167","wikidata":"https://www.wikidata.org/wiki/Q910164","display_name":"Cheminformatics","level":2,"score":0.3066999912261963},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2822999954223633},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.27549999952316284},{"id":"https://openalex.org/C2991944050","wikidata":"https://www.wikidata.org/wiki/Q36534","display_name":"Reaction conditions","level":3,"score":0.26919999718666077},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2651999890804291},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.26429998874664307},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.2639999985694885},{"id":"https://openalex.org/C99726746","wikidata":"https://www.wikidata.org/wiki/Q906396","display_name":"Chemical space","level":3,"score":0.25429999828338623},{"id":"https://openalex.org/C177801218","wikidata":"https://www.wikidata.org/wiki/Q36534","display_name":"Chemical reaction","level":2,"score":0.2500999867916107}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3746252.3761236","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3746252.3761236","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 34th ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3746252.3761236","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3746252.3761236","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 34th ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1975147762","https://openalex.org/W1988037271","https://openalex.org/W1997974358","https://openalex.org/W2046747992","https://openalex.org/W2324964582","https://openalex.org/W2551217916","https://openalex.org/W2909063104","https://openalex.org/W2963396480","https://openalex.org/W2964105864","https://openalex.org/W2966357564","https://openalex.org/W3036578548","https://openalex.org/W3038856956","https://openalex.org/W3117879109","https://openalex.org/W3130274530","https://openalex.org/W3181403764","https://openalex.org/W3209726219","https://openalex.org/W4205455490","https://openalex.org/W4220902634","https://openalex.org/W4226159083","https://openalex.org/W4226227198","https://openalex.org/W4281558957","https://openalex.org/W4286901673","https://openalex.org/W4365397996","https://openalex.org/W4393158261"],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"chemical":[1],"reaction":[2,23,40,49,109],"prediction":[3,41],"is":[4],"essential":[5],"for":[6,65,71,78],"drug":[7],"discovery":[8],"and":[9,75],"synthetic":[10],"planning.":[11],"However,":[12],"this":[13,31],"task":[14],"becomes":[15],"particularly":[16],"challenging":[17],"in":[18,89,124,129],"low-data":[19],"scenarios,":[20],"where":[21],"novel":[22,37],"types":[24,50],"lack":[25],"sufficient":[26],"training":[27,55],"examples.":[28],"To":[29],"address":[30],"challenge,":[32],"we":[33],"propose":[34],"FewRxn,":[35],"a":[36,53],"model-agnostic":[38],"few-shot":[39,90,130],"framework":[42],"that":[43,113],"enables":[44],"rapid":[45],"adaptation":[46],"to":[47],"unseen":[48],"using":[51],"only":[52],"few":[54],"samples.":[56],"FewRxn":[57,85],"integrates":[58],"several":[59],"key":[60],"innovations,":[61],"including":[62],"segmentation":[63],"masks":[64],"enhanced":[66],"reactant":[67],"representation,":[68],"fingerprint":[69],"embeddings":[70],"richer":[72],"molecular":[73,106,117],"context,":[74],"task-aware":[76],"meta-learning":[77],"effective":[79],"knowledge":[80,110,114],"transfer.":[81],"Through":[82],"extensive":[83],"evaluations,":[84],"achieves":[86],"state-of-the-art":[87],"accuracy":[88],"settings,":[91],"significantly":[92],"outperforming":[93],"traditional":[94],"fine-tuning":[95],"methods.":[96],"Additionally,":[97],"our":[98],"work":[99],"provides":[100],"insights":[101],"into":[102],"the":[103],"impact":[104],"of":[105,126],"representations":[107],"on":[108],"transfer,":[111],"demonstrating":[112],"captured":[115],"under":[116],"graph-based":[118],"formulation":[119],"consistently":[120],"outperforms":[121],"those":[122],"learned":[123],"forms":[125],"SMILES":[127],"generation":[128],"learning.":[131]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-11-08T00:00:00"}