{"id":"https://openalex.org/W7165678727","doi":"https://doi.org/10.48550/arxiv.2606.23361","title":"Rethinking Molecular Graph Backdoors under Chemistry-aware Admission","display_name":"Rethinking Molecular Graph Backdoors under Chemistry-aware Admission","publication_year":2026,"publication_date":"2026-06-22","ids":{"openalex":"https://openalex.org/W7165678727","doi":"https://doi.org/10.48550/arxiv.2606.23361"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.23361","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.23361","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.2606.23361","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138723043","display_name":"Thinh T. H. Nguyen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nguyen, Thinh T. H.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139134145","display_name":"Sze Jue Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Sze Jue","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080642445","display_name":"Khoa D. Doan","orcid":"https://orcid.org/0000-0002-1610-8206"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Doan, Khoa D.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070805897","display_name":"Chee Seng Chan","orcid":"https://orcid.org/0000-0001-7677-2865"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chan, Chee Seng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5129666703","display_name":"Kok-Seng Wong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wong, Kok-Seng","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/T11273","display_name":"Advanced Graph Neural Networks","score":0.9031999707221985,"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"}},"topics":[{"id":"https://openalex.org/T11273","display_name":"Advanced Graph Neural Networks","score":0.9031999707221985,"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":0.026399999856948853,"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":0.01860000006854534,"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/backdoor","display_name":"Backdoor","score":0.8555999994277954},{"id":"https://openalex.org/keywords/molecular-graph","display_name":"Molecular graph","score":0.5066999793052673},{"id":"https://openalex.org/keywords/string","display_name":"String (physics)","score":0.4943000078201294},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4666000008583069},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.4269999861717224},{"id":"https://openalex.org/keywords/scheme","display_name":"Scheme (mathematics)","score":0.34599998593330383}],"concepts":[{"id":"https://openalex.org/C2781045450","wikidata":"https://www.wikidata.org/wiki/Q254569","display_name":"Backdoor","level":2,"score":0.8555999994277954},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6873999834060669},{"id":"https://openalex.org/C2780022179","wikidata":"https://www.wikidata.org/wiki/Q1986794","display_name":"Molecular graph","level":3,"score":0.5066999793052673},{"id":"https://openalex.org/C157486923","wikidata":"https://www.wikidata.org/wiki/Q1376436","display_name":"String (physics)","level":2,"score":0.4943000078201294},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4666000008583069},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.44020000100135803},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.4269999861717224},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.34599998593330383},{"id":"https://openalex.org/C2780385302","wikidata":"https://www.wikidata.org/wiki/Q367158","display_name":"Protocol (science)","level":3,"score":0.3424000144004822},{"id":"https://openalex.org/C74197172","wikidata":"https://www.wikidata.org/wiki/Q1195339","display_name":"Directed acyclic graph","level":2,"score":0.3336000144481659},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.3149000108242035},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.2964000105857849},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.29490000009536743},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.28029999136924744},{"id":"https://openalex.org/C64357122","wikidata":"https://www.wikidata.org/wiki/Q1149766","display_name":"Causality (physics)","level":2,"score":0.2736999988555908},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.26930001378059387},{"id":"https://openalex.org/C146380142","wikidata":"https://www.wikidata.org/wiki/Q1137726","display_name":"Directed graph","level":2,"score":0.2639999985694885}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.23361","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.23361","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.2606.23361","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.23361","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":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Backdoor":[0],"attacks":[1],"on":[2,23],"molecular":[3,17,54,73,87,123,130,158,181,217],"graph":[4,13,79],"neural":[5],"networks":[6],"(GNNs)":[7],"are":[8,106,118],"typically":[9],"evaluated":[10],"as":[11,44],"abstract":[12],"edits,":[14],"but":[15,167],"real":[16],"learning":[18,60],"pipelines":[19],"do":[20],"not":[21],"train":[22],"arbitrary":[24],"graphs.":[25],"Molecular":[26],"records":[27],"must":[28],"first":[29],"survive":[30],"parsing,":[31],"sanitization,":[32],"canonicalization,":[33],"and":[34,77,139,163,185,215],"graph-string":[35],"consistency":[36],"checks.":[37],"We":[38,111,125],"formalize":[39],"this":[40,90],"overlooked":[41],"admission":[42,115,207],"stage":[43],"ChemGuard,":[45],"an":[46,128],"operational":[47,91],"protocol":[48],"for":[49],"testing":[50],"whether":[51],"a":[52,58,68,203,220],"submitted":[53,86],"record":[55,69],"can":[56],"enter":[57],"realistic":[59],"pipeline,":[61],"while":[62,196],"complementing":[63],"existing":[64,94],"defenses.":[65],"ChemGuard":[66],"admits":[67],"only":[70],"when":[71],"its":[72],"string":[74,83],"is":[75,152],"sanitizable":[76],"the":[78,85],"reconstructed":[80],"from":[81],"that":[82,114,133],"matches":[84],"graph.":[88],"Under":[89],"view,":[92],"many":[93,209],"graph-based":[95],"backdoors":[96,218],"lose":[97],"much":[98],"of":[99],"their":[100,104],"apparent":[101],"efficacy":[102],"because":[103],"poisons":[105,195],"chemically":[107,135,213],"invalid":[108],"or":[109,177],"representation-inconsistent.":[110],"then":[112],"show":[113],"checks":[116],"alone":[117],"insufficient":[119],"to":[120,147],"rule":[121],"out":[122],"backdoors.":[124],"propose":[126],"ChemBack,":[127],"admission-aware":[129],"backdoor":[131],"attack":[132,190],"constructs":[134],"feasible":[136],"motif-anchor":[137],"attachments":[138],"ranks":[140],"admitted":[141,194],"candidates":[142],"by":[143],"fingerprint-based":[144],"Tanimoto":[145],"similarity":[146],"clean":[148,198],"target-class":[149],"molecules.":[150],"ChemBack":[151],"model-free":[153],"during":[154],"trigger":[155],"selection,":[156],"using":[157],"structures,":[159],"target":[160],"labels,":[161],"fingerprints,":[162],"public":[164],"validity":[165],"checks,":[166],"no":[168],"victim":[169],"model,":[170],"surrogate":[171],"GNN,":[172],"learned":[173],"embedding,":[174],"gradient,":[175],"logit,":[176],"training-code":[178],"access.":[179],"Across":[180],"benchmarks,":[182],"validators,":[183],"architectures,":[184],"defenses,":[186],"\\textbf{ChemBack}":[187],"achieves":[188],"high":[189],"success":[191],"with":[192],"fully":[193],"preserving":[197],"accuracy.":[199],"Our":[200],"results":[201],"reveal":[202],"two-sided":[204],"lesson,":[205],"chemistry-aware":[206],"suppresses":[208],"graph-only":[210],"backdoors,":[211],"yet":[212],"valid":[214],"target-aligned":[216],"remain":[219],"practical":[221],"threat.":[222]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-24T00:00:00"}