{"id":"https://openalex.org/W7151567562","doi":"https://doi.org/10.48550/arxiv.2604.03361","title":"The limits of bio-molecular modeling with large language models : a cross-scale evaluation","display_name":"The limits of bio-molecular modeling with large language models : a cross-scale evaluation","publication_year":2026,"publication_date":"2026-04-03","ids":{"openalex":"https://openalex.org/W7151567562","doi":"https://doi.org/10.48550/arxiv.2604.03361"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.03361","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.03361","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":null,"license_id":null,"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.2604.03361","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5133147706","display_name":"Yaxin Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Yaxin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133145358","display_name":"Yue Zhou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhou, Yue","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133136336","display_name":"Tianyu Zhao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhao, Tianyu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069123107","display_name":"Fengwei An","orcid":"https://orcid.org/0000-0002-7554-7938"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"An, Fengwei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133096171","display_name":"Zhixiang Ren","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ren, Zhixiang","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/T12254","display_name":"Machine Learning in Bioinformatics","score":0.311599999666214,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T12254","display_name":"Machine Learning in Bioinformatics","score":0.311599999666214,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.21699999272823334,"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":0.10090000182390213,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.4489000141620636},{"id":"https://openalex.org/keywords/computational-model","display_name":"Computational model","score":0.39660000801086426},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.3167000114917755},{"id":"https://openalex.org/keywords/mathematical-model","display_name":"Mathematical model","score":0.2903999984264374},{"id":"https://openalex.org/keywords/model-validation","display_name":"Model validation","score":0.28679999709129333}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.711899995803833},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.48100000619888306},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.4489000141620636},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4016000032424927},{"id":"https://openalex.org/C66024118","wikidata":"https://www.wikidata.org/wiki/Q1122506","display_name":"Computational model","level":2,"score":0.39660000801086426},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.3167000114917755},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.30070000886917114},{"id":"https://openalex.org/C76969082","wikidata":"https://www.wikidata.org/wiki/Q486902","display_name":"Mathematical model","level":2,"score":0.2903999984264374},{"id":"https://openalex.org/C3019813237","wikidata":"https://www.wikidata.org/wiki/Q65089264","display_name":"Model validation","level":2,"score":0.28679999709129333},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.2856000065803528},{"id":"https://openalex.org/C112930515","wikidata":"https://www.wikidata.org/wiki/Q4389547","display_name":"Risk analysis (engineering)","level":1,"score":0.28299999237060547},{"id":"https://openalex.org/C108215451","wikidata":"https://www.wikidata.org/wiki/Q7263963","display_name":"Simulation modeling","level":2,"score":0.2624000012874603},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.25279998779296875}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.03361","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.03361","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2604.03361","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.03361","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"The":[0],"modeling":[1,147],"of":[2,35,121,148],"bio-molecular":[3,23,56,112],"system":[4],"across":[5,28],"molecular":[6,149],"scales":[7],"remains":[8],"a":[9,43,59,78],"central":[10],"challenge":[11],"in":[12],"scientific":[13],"research.":[14],"Large":[15],"language":[16],"models":[17,86],"(LLMs)":[18],"are":[19,75,107],"increasingly":[20],"applied":[21],"to":[22],"discovery,":[24],"yet":[25],"systematic":[26,44],"evaluation":[27],"multi-scale":[29],"biological":[30,102],"problems":[31],"and":[32,49,72,96,123],"rigorous":[33],"assessment":[34],"their":[36],"tool-augmented":[37],"capabilities":[38],"remain":[39,132],"limited.":[40],"We":[41],"reveal":[42],"gap":[45],"between":[46],"LLM":[47],"performance":[48,100],"mechanistic":[50],"understanding":[51],"through":[52],"the":[53,119],"proposed":[54],"cross-scale":[55],"benchmark:":[57],"BioMol-LLM-Bench,":[58],"unified":[60],"framework":[61],"comprising":[62],"26":[63],"downstream":[64],"tasks":[65,130],"that":[66],"covers":[67],"4":[68,88],"distinct":[69],"difficulty":[70],"levels,":[71],"computational":[73],"tools":[74],"integrated":[76],"for":[77,110,144],"more":[79,108],"comprehensive":[80],"evaluation.":[81],"Evaluation":[82],"on":[83,101,128,134],"13":[84],"representative":[85],"reveals":[87],"main":[89],"findings:":[90],"chain-of-thought":[91],"data":[92],"provides":[93],"limited":[94],"benefit":[95],"may":[97],"even":[98],"reduce":[99],"tasks;":[103],"hybrid":[104],"mamba-attention":[105],"architectures":[106],"effective":[109],"long":[111],"sequences;":[113],"supervised":[114],"fine-tuning":[115],"improves":[116],"specialization":[117],"at":[118],"cost":[120],"generalization;":[122],"current":[124],"LLMs":[125],"perform":[126],"well":[127],"classification":[129],"but":[131],"weak":[133],"challenging":[135],"regression":[136],"tasks.":[137],"Together,":[138],"these":[139],"findings":[140],"provide":[141],"practical":[142],"guidance":[143],"future":[145],"LLM-based":[146],"systems.":[150]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-04-08T00:00:00"}