{"id":"https://openalex.org/W7165184172","doi":"https://doi.org/10.48550/arxiv.2606.18390","title":"MOLAR: Learning Multimodal Molecular Representations from Noisy Labels","display_name":"MOLAR: Learning Multimodal Molecular Representations from Noisy Labels","publication_year":2026,"publication_date":"2026-06-16","ids":{"openalex":"https://openalex.org/W7165184172","doi":"https://doi.org/10.48550/arxiv.2606.18390"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.18390","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.18390","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.18390","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138941127","display_name":"Yingxu Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Yingxu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138832656","display_name":"Kunyu Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Kunyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138831637","display_name":"Nan Yin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yin, Nan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138954929","display_name":"Yu Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Yu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5012450539","display_name":"Eran Segal","orcid":"https://orcid.org/0000-0002-6859-1164"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Segal, Eran","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10211","display_name":"Computational Drug Discovery Methods","score":0.3422999978065491,"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":0.3422999978065491,"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/T12535","display_name":"Machine Learning and Data Classification","score":0.23649999499320984,"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/T11948","display_name":"Machine Learning in Materials Science","score":0.12970000505447388,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/categorical-variable","display_name":"Categorical variable","score":0.5327000021934509},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5169000029563904},{"id":"https://openalex.org/keywords/annotation","display_name":"Annotation","score":0.503000020980835},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.46700000762939453},{"id":"https://openalex.org/keywords/property","display_name":"Property (philosophy)","score":0.4652000069618225},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.436599999666214},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.43630000948905945},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.42750000953674316}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6725999712944031},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6180999875068665},{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.5327000021934509},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5169000029563904},{"id":"https://openalex.org/C2776321320","wikidata":"https://www.wikidata.org/wiki/Q857525","display_name":"Annotation","level":2,"score":0.503000020980835},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.46700000762939453},{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.4652000069618225},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.436599999666214},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.43630000948905945},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.42750000953674316},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4185999929904938},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.40860000252723694},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.3978999853134155},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.3750999867916107},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.3528999984264374},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.3450999855995178},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3264000117778778},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.326200008392334},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.3118000030517578},{"id":"https://openalex.org/C2778828372","wikidata":"https://www.wikidata.org/wiki/Q5283209","display_name":"Distributional semantics","level":3,"score":0.2766999900341034},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.27489998936653137},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.26019999384880066},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.2563999891281128}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.18390","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.18390","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.18390","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.18390","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.4316950738430023}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Motivation:":[0],"Noisy":[1],"labels":[2,34,116],"are":[3,14],"a":[4,76,103,107],"common":[5],"challenge":[6],"in":[7],"molecular":[8,12,48,52,82,127,136],"property":[9],"prediction":[10],"because":[11],"annotations":[13],"often":[15],"obtained":[16],"from":[17,84,92,129],"assays,":[18],"curated":[19],"databases,":[20],"or":[21,63],"weak":[22],"annotation":[23],"pipelines":[24],"rather":[25],"than":[26],"directly":[27],"observed":[28],"clean":[29],"biological":[30],"states.":[31],"Treating":[32],"recorded":[33,115],"as":[35],"reliable":[36],"supervision":[37],"can":[38,57],"cause":[39],"models":[40],"to":[41,102,114],"memorize":[42],"corrupted":[43],"observations":[44],"and":[45,96,106,125,138,158],"learn":[46],"misleading":[47],"evidence.":[49],"In":[50],"multimodal":[51,81],"representation":[53],"learning,":[54],"this":[55,112],"issue":[56],"be":[58],"amplified":[59],"by":[60],"graph-text":[61],"fusion":[62],"alignment,":[64],"which":[65],"may":[66],"propagate":[67],"label-induced":[68],"errors":[69],"across":[70],"modalities.":[71],"Results:":[72],"We":[73],"propose":[74],"MOLAR,":[75],"noise-aware":[77],"framework":[78],"for":[79,117],"learning":[80],"representations":[83],"noisy":[85,135],"labels.":[86],"MOLAR":[87,144,154],"separates":[88],"latent":[89],"clean-property":[90,104],"inference":[91],"recorded-label":[93],"observation:":[94],"graph":[95],"text":[97],"views":[98],"contribute":[99],"residual":[100],"evidence":[101,128],"distribution,":[105],"categorical":[108],"label-observation":[109],"channel":[110],"maps":[111],"distribution":[113],"training.":[118],"This":[119],"formulation":[120],"derives":[121],"posterior":[122],"label":[123],"reliability":[124,157],"modality-specific":[126],"the":[130],"model.":[131],"Experiments":[132],"on":[133],"naturally":[134],"benchmarks":[137,141],"controlled":[139],"label-flipping":[140],"show":[142,152],"that":[143,153],"consistently":[145],"outperforms":[146],"representative":[147],"baselines.":[148],"Visualization":[149],"analyses":[150],"further":[151],"provides":[155],"interpretable":[156],"modality-evidence":[159],"diagnostics.":[160]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-19T00:00:00"}