{"id":"https://openalex.org/W7161151140","doi":"https://doi.org/10.48550/arxiv.2605.13101","title":"Margin-calibrated Classifier Guidance for Property-driven Synthesis Planning","display_name":"Margin-calibrated Classifier Guidance for Property-driven Synthesis Planning","publication_year":2026,"publication_date":"2026-05-13","ids":{"openalex":"https://openalex.org/W7161151140","doi":"https://doi.org/10.48550/arxiv.2605.13101"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.13101","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.13101","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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.13101","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5136167416","display_name":"Najwa Laabid","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Laabid, Najwa","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5013326774","display_name":"Vikas Garg","orcid":"https://orcid.org/0000-0002-9713-179X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Garg, Vikas","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"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/T11948","display_name":"Machine Learning in Materials Science","score":0.9334999918937683,"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":0.9334999918937683,"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":0.011300000362098217,"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/T10857","display_name":"Advanced Electron Microscopy Techniques and Applications","score":0.004399999976158142,"subfield":{"id":"https://openalex.org/subfields/1315","display_name":"Structural 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/classifier","display_name":"Classifier (UML)","score":0.7807999849319458},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5491999983787537},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4440999925136566},{"id":"https://openalex.org/keywords/sequence","display_name":"Sequence (biology)","score":0.40400001406669617},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.3463999927043915},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3215999901294708},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.31790000200271606}],"concepts":[{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.7807999849319458},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6802999973297119},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6227999925613403},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5529999732971191},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5491999983787537},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4440999925136566},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.40400001406669617},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.3463999927043915},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3215999901294708},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.31790000200271606},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.30820000171661377},{"id":"https://openalex.org/C2780735816","wikidata":"https://www.wikidata.org/wiki/Q28324931","display_name":"Incremental learning","level":2,"score":0.2847999930381775},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.28360000252723694},{"id":"https://openalex.org/C52620605","wikidata":"https://www.wikidata.org/wiki/Q7268357","display_name":"Quadratic classifier","level":3,"score":0.2827000021934509},{"id":"https://openalex.org/C69738355","wikidata":"https://www.wikidata.org/wiki/Q1228929","display_name":"Linear discriminant analysis","level":2,"score":0.2587999999523163},{"id":"https://openalex.org/C1921717","wikidata":"https://www.wikidata.org/wiki/Q1334846","display_name":"Mahalanobis distance","level":2,"score":0.2533999979496002},{"id":"https://openalex.org/C78397625","wikidata":"https://www.wikidata.org/wiki/Q192487","display_name":"Discriminant","level":2,"score":0.25200000405311584},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.25130000710487366}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.13101","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.13101","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":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.13101","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.13101","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":"article"},"sustainable_development_goals":[{"score":0.6581881642341614,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Synthesis":[0],"planning":[1],"seeks":[2],"an":[3],"efficient":[4],"sequence":[5],"of":[6,38,64,129],"chemical":[7],"reactions":[8,42],"that":[9,43,110,122],"produce":[10],"a":[11,15,27,89,102],"target":[12],"molecule.":[13],"Typically,":[14],"pretrained":[16],"single-step":[17,39],"(autoregressive)":[18],"retrosynthesis":[19],"model":[20,40],"is":[21],"repeatedly":[22],"invoked":[23],"to":[24,55,71,105,154],"generate":[25],"such":[26],"sequence.":[28],"Classifier":[29],"guidance":[30,142,158],"can,":[31],"in":[32],"principle,":[33],"help":[34],"steer":[35],"the":[36,57,62,73,107,127,180],"output":[37],"toward":[41],"satisfy":[44],"specific":[45],"constraints":[46],"or":[47],"accommodate":[48],"chemist's":[49],"preferences":[50],"during":[51,117],"inference":[52],"without":[53],"having":[54],"retrain":[56],"autoregressive":[58],"generator.":[59],"We":[60,84,119],"expose":[61],"insufficiency":[63],"auxiliary":[65],"classifiers":[66,124],"trained":[67],"with":[68,88,156,161,173],"cross-entropy":[69],"loss":[70,104],"override":[72],"unconditional":[74],"token-level":[75],"distributions":[76],"learned":[77],"from":[78,150],"typical":[79],"sparse":[80],"single-disconnection":[81],"reaction":[82],"datasets.":[83],"overcome":[85],"this":[86],"issue":[87],"novel":[90],"method":[91,176],"called":[92],"Sequence":[93],"Completion":[94],"Ranking":[95],"(SCR),":[96],"which":[97],"employs":[98],"contrastive":[99],"argumentation":[100],"and":[101,159,186],"margin-based":[103],"calibrate":[106],"classifier":[108],"so":[109],"it":[111],"can":[112,125],"meaningfully":[113],"discriminate":[114],"between":[115,184],"continuations":[116],"decoding.":[118],"formally":[120],"establish":[121],"margin-calibrated":[123],"expand":[126],"set":[128],"property-satisfying":[130],"sequences":[131],"reachable":[132],"under":[133],"guided":[134],"beam":[135],"search.":[136],"Empirically,":[137],"on":[138],"USPTO-190,":[139],"given":[140],"chemist-specified":[141],"targets,":[143],"SCR":[144],"substantially":[145],"improves":[146],"multi-step":[147],"solve":[148],"rates":[149],"$16.8\\%$":[151],"(unguided":[152],"generator)":[153],"$78.4\\%$":[155],"reaction-type":[157],"$95.3\\%$":[160],"Tanimoto":[162],"guidance,":[163],"unlocking":[164],"valid":[165],"routes":[166],"for":[167],"33":[168],"targets":[169],"($17.4\\%$)":[170],"previously":[171],"unsolvable":[172],"baselines.":[174],"Our":[175],"also":[177],"effectively":[178],"closes":[179],"long-standing":[181],"diversity":[182],"gap":[183],"template-free":[185],"template-based":[187],"methods.":[188]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2026-05-15T00:00:00"}
