{"id":"https://openalex.org/W7162787759","doi":"https://doi.org/10.48550/arxiv.2605.29108","title":"Bridging Chemists and AI: An Expert-Augmented Framework for Interpretable Route Evaluation","display_name":"Bridging Chemists and AI: An Expert-Augmented Framework for Interpretable Route Evaluation","publication_year":2026,"publication_date":"2026-05-27","ids":{"openalex":"https://openalex.org/W7162787759","doi":"https://doi.org/10.48550/arxiv.2605.29108"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.29108","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.29108","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.2605.29108","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137346054","display_name":"Yujia Guo","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guo, Yujia","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137351387","display_name":"Mikhail Kabeshov","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kabeshov, Mikhail","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109667655","display_name":"Tat Hong Duong Le","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Le, Tat Hong Duong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006219866","display_name":"Samuel Genheden","orcid":"https://orcid.org/0000-0002-7624-7363"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Genheden, Samuel","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5036379650","display_name":"Marco V. Mijangos","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mijangos, Marco V.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125711583","display_name":"Varvara Voinarvoska","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Voinarvoska, Varvara","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5072881705","display_name":"Giulia Bergonzini","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bergonzini, Giulia","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076975589","display_name":"Ola Engkvist","orcid":"https://orcid.org/0000-0003-4970-6461"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Engkvist, Ola","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137333135","display_name":"Samuel Kaski","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kaski, Samuel","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/T11948","display_name":"Machine Learning in Materials Science","score":0.8855999708175659,"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":0.8855999708175659,"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":0.04729999974370003,"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/T13180","display_name":"Chemistry and Chemical Engineering","score":0.014499999582767487,"subfield":{"id":"https://openalex.org/subfields/2304","display_name":"Environmental Chemistry"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/bridging","display_name":"Bridging (networking)","score":0.7440000176429749},{"id":"https://openalex.org/keywords/ranking","display_name":"Ranking (information retrieval)","score":0.5719000101089478},{"id":"https://openalex.org/keywords/bridge","display_name":"Bridge (graph theory)","score":0.42739999294281006},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.4174000024795532},{"id":"https://openalex.org/keywords/correlation-coefficient","display_name":"Correlation coefficient","score":0.399399995803833},{"id":"https://openalex.org/keywords/proxy","display_name":"Proxy (statistics)","score":0.39719998836517334},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.38109999895095825},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.37380000948905945}],"concepts":[{"id":"https://openalex.org/C174348530","wikidata":"https://www.wikidata.org/wiki/Q188635","display_name":"Bridging (networking)","level":2,"score":0.7440000176429749},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6244999766349792},{"id":"https://openalex.org/C189430467","wikidata":"https://www.wikidata.org/wiki/Q7293293","display_name":"Ranking (information retrieval)","level":2,"score":0.5719000101089478},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5410000085830688},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5404999852180481},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4767000079154968},{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.42739999294281006},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.4174000024795532},{"id":"https://openalex.org/C2780092901","wikidata":"https://www.wikidata.org/wiki/Q3433612","display_name":"Correlation coefficient","level":2,"score":0.399399995803833},{"id":"https://openalex.org/C2780148112","wikidata":"https://www.wikidata.org/wiki/Q1432581","display_name":"Proxy (statistics)","level":2,"score":0.39719998836517334},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.38109999895095825},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.37380000948905945},{"id":"https://openalex.org/C55078378","wikidata":"https://www.wikidata.org/wiki/Q1136628","display_name":"Pearson product-moment correlation coefficient","level":2,"score":0.36739999055862427},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.36559998989105225},{"id":"https://openalex.org/C159744936","wikidata":"https://www.wikidata.org/wiki/Q1126730","display_name":"Spearman's rank correlation coefficient","level":2,"score":0.3601999878883362},{"id":"https://openalex.org/C58328972","wikidata":"https://www.wikidata.org/wiki/Q184609","display_name":"Expert system","level":2,"score":0.35670000314712524},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.33390000462532043},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.3271999955177307},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.30320000648498535},{"id":"https://openalex.org/C133462117","wikidata":"https://www.wikidata.org/wiki/Q4929239","display_name":"Data collection","level":2,"score":0.27090001106262207},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2653000056743622},{"id":"https://openalex.org/C148524875","wikidata":"https://www.wikidata.org/wiki/Q6975395","display_name":"F1 score","level":2,"score":0.2554999887943268},{"id":"https://openalex.org/C3018587665","wikidata":"https://www.wikidata.org/wiki/Q7268696","display_name":"Qualitative analysis","level":3,"score":0.2542000114917755}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.29108","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.29108","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.2605.29108","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.29108","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":[{"display_name":"Industry, innovation and infrastructure","score":0.6107659935951233,"id":"https://metadata.un.org/sdg/9"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Selecting":[0],"efficient":[1],"multi-step":[2],"synthetic":[3],"routes":[4],"is":[5,81],"a":[6,115,122],"central":[7],"challenge":[8],"in":[9,13],"organic":[10],"synthesis,":[11],"particularly":[12],"medicinal":[14],"and":[15,25,39,74,89,92,103,109,121,131],"process":[16],"chemistry,":[17],"where":[18],"route":[19,76],"choice":[20],"directly":[21],"impacts":[22],"feasibility,":[23],"cost,":[24],"development":[26],"efficiency.":[27],"Data-driven":[28],"assessment":[29,129],"systems":[30],"often":[31],"oversimplify":[32],"the":[33,141],"multi-objective":[34],"nature":[35],"of":[36,119,125,144],"synthesis":[37],"design":[38],"rely":[40],"on":[41],"proxy":[42],"datasets,":[43],"such":[44],"as":[45],"patent":[46],"routes,":[47,91],"rather":[48],"than":[49],"universally":[50],"grounded":[51],"criteria.":[52],"To":[53],"address":[54],"this,":[55],"we":[56],"introduce":[57],"an":[58],"expert-augmented,":[59],"data-driven":[60],"scoring":[61],"framework":[62],"that":[63],"integrates":[64],"machine":[65],"learning":[66],"with":[67,95],"chemists'":[68],"domain":[69],"knowledge":[70],"for":[71,127,136],"both":[72,100],"numerical":[73],"explainable":[75],"assessment.":[77],"A":[78],"DeepSets-based":[79],"model":[80],"trained":[82],"using":[83],"tree":[84],"edit":[85],"distance":[86],"between":[87],"reference":[88],"machine-generated":[90],"then":[93],"fine-tuned":[94],"expert":[96],"evaluations":[97],"to":[98],"produce":[99],"quantitative":[101],"scores":[102],"interpretable":[104],"qualitative":[105],"categories:":[106],"Good,":[107],"Plausible,":[108],"Bad.":[110],"The":[111],"resulting":[112],"system":[113],"achieves":[114],"Spearman":[116],"correlation":[117,124],"coefficient":[118],"0.78":[120],"Pearson":[123],"0.77":[126],"category":[128],"prediction,":[130,138],"60.2%":[132],"top-1":[133],"ranking":[134],"accuracy":[135],"score":[137],"substantially":[139],"outperforming":[140],"previous":[142],"baseline":[143],"17.5%.":[145]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-30T00:00:00"}
