{"id":"https://openalex.org/W7161731387","doi":"https://doi.org/10.48550/arxiv.2605.17265","title":"When Molecular Similarity Works: Property Cliffs Reveal Hidden Errors","display_name":"When Molecular Similarity Works: Property Cliffs Reveal Hidden Errors","publication_year":2026,"publication_date":"2026-05-17","ids":{"openalex":"https://openalex.org/W7161731387","doi":"https://doi.org/10.48550/arxiv.2605.17265"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.17265","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.17265","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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.2605.17265","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5018121459","display_name":"Di Hu","orcid":"https://orcid.org/0000-0003-4525-2255"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hu, Di","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136503660","display_name":"Kun Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Kun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071315437","display_name":"Haojie Rao","orcid":"https://orcid.org/0009-0006-9156-2480"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rao, Haojie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129747631","display_name":"Longtao Hu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hu, Longtao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136484844","display_name":"Jiameng Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Jiameng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136496097","display_name":"Wenbin Hu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hu, Wenbin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136501338","display_name":"Yizhen Zheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zheng, Yizhen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136462704","display_name":"Jiajun Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Jiajun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5110996824","display_name":"Duanhua Cao","orcid":"https://orcid.org/0009-0006-0140-7998"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cao, Duanhua","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":0.9258000254631042,"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":0.9258000254631042,"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/T11948","display_name":"Machine Learning in Materials Science","score":0.05209999904036522,"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/T10044","display_name":"Protein Structure and Dynamics","score":0.008100000210106373,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/property","display_name":"Property (philosophy)","score":0.7421000003814697},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.6604999899864197},{"id":"https://openalex.org/keywords/aggregate","display_name":"Aggregate (composite)","score":0.5437999963760376},{"id":"https://openalex.org/keywords/mechanism","display_name":"Mechanism (biology)","score":0.45570001006126404},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.4440999925136566},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.4196000099182129},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.36500000953674316}],"concepts":[{"id":"https://openalex.org/C189950617","wikidata":"https://www.wikidata.org/wiki/Q937228","display_name":"Property (philosophy)","level":2,"score":0.7421000003814697},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.6604999899864197},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.638700008392334},{"id":"https://openalex.org/C4679612","wikidata":"https://www.wikidata.org/wiki/Q866298","display_name":"Aggregate (composite)","level":2,"score":0.5437999963760376},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5192999839782715},{"id":"https://openalex.org/C89611455","wikidata":"https://www.wikidata.org/wiki/Q6804646","display_name":"Mechanism (biology)","level":2,"score":0.45570001006126404},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.4440999925136566},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.4196000099182129},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.36500000953674316},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.36500000953674316},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.36480000615119934},{"id":"https://openalex.org/C2780385302","wikidata":"https://www.wikidata.org/wiki/Q367158","display_name":"Protocol (science)","level":3,"score":0.35850000381469727},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3560999929904938},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.35019999742507935},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3312999904155731},{"id":"https://openalex.org/C201399114","wikidata":"https://www.wikidata.org/wiki/Q898401","display_name":"Lipophilicity","level":2,"score":0.31040000915527344},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.28929999470710754},{"id":"https://openalex.org/C14314382","wikidata":"https://www.wikidata.org/wiki/Q1943386","display_name":"Molecular Pharmacology","level":3,"score":0.26409998536109924},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.2533999979496002},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.25209999084472656},{"id":"https://openalex.org/C2777093003","wikidata":"https://www.wikidata.org/wiki/Q6508345","display_name":"Lead (geology)","level":2,"score":0.2500999867916107}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.17265","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.17265","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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.2605.17265","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.17265","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"prediction":[1],"of":[2],"molecular":[3,29,151,164],"properties":[4],"underpins":[5],"drug":[6],"discovery":[7],"and":[8,74,91,108,141],"material":[9],"design,":[10],"yet":[11],"even":[12],"state-of-the-art":[13],"models":[14,61],"remain":[15],"vulnerable":[16],"to":[17,137],"localized":[18],"failure":[19,77,153],"modes":[20],"that":[21,84,116],"aggregate":[22],"metrics":[23],"cannot":[24],"detect.":[25],"The":[26,167],"places":[27,37],"where":[28,38],"similarity":[30,152],"should":[31],"be":[32,42],"most":[33,43],"helpful":[34],"are":[35,101],"also":[36],"standard":[39],"evaluation":[40,82,161],"can":[41,53],"misleading.":[44],"Property":[45],"cliffs":[46],"expose":[47,73],"this":[48,76],"gap:":[49],"structurally":[50],"similar":[51],"molecules":[52],"still":[54],"differ":[55],"sharply":[56],"in":[57,68,124],"target":[58],"property,":[59],"so":[60],"with":[62],"competitive":[63],"overall":[64,143],"performance":[65],"may":[66],"fail":[67],"high-risk":[69],"local":[70],"neighborhoods.":[71],"To":[72],"mitigate":[75],"mode,":[78],"CliffSplit,":[79],"a":[80,93,155,159],"cliff-aware":[81],"protocol":[83],"constructs":[85],"locally":[86],"supported,":[87],"cliff-exposed":[88],"test":[89],"cases,":[90],"CliffLoss,":[92],"model-agnostic":[94],"train-only":[95],"mitigation":[96],"mechanism":[97],"for":[98,163],"cliff-sensitive":[99],"errors,":[100],"introduced.":[102],"Experiments":[103],"on":[104,139],"three":[105,109],"QM9":[106,126],"targets":[107],"MoleculeNet":[110],"tasks":[111],"across":[112],"five":[113],"backbones":[114],"show":[115],"CliffSplit":[117],"reveals":[118],"at":[119,171],"least":[120],"15%":[121],"higher":[122],"error":[123,133],"cliff-heavy":[125],"regions,":[127],"while":[128],"CliffLoss":[129],"reduces":[130],"the":[131],"cliff-to-smooth":[132],"gap":[134],"by":[135,145],"up":[136],"30%":[138],"Lipophilicity":[140],"improves":[142],"MAE":[144],"9.7%.":[146],"Together,":[147],"these":[148],"results":[149],"turn":[150],"from":[154],"descriptive":[156],"anomaly":[157],"into":[158],"benchmarked":[160],"problem":[162],"machine":[165],"learning.":[166],"code":[168],"is":[169],"available":[170],"https://anonymous.4open.science/r/Cliff_Loss.":[172]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-20T00:00:00"}
