{"id":"https://openalex.org/W7133196486","doi":"https://doi.org/10.52202/075280-0178","title":"AI for Interpretable Chemistry: Predicting Radical Mechanistic Pathways via Contrastive Learning","display_name":"AI for Interpretable Chemistry: Predicting Radical Mechanistic Pathways via Contrastive Learning","publication_year":2023,"publication_date":"2023-01-01","ids":{"openalex":"https://openalex.org/W7133196486","doi":"https://doi.org/10.52202/075280-0178"},"language":"en","primary_location":{"id":"doi:10.52202/075280-0178","doi":"https://doi.org/10.52202/075280-0178","is_oa":false,"landing_page_url":"https://doi.org/10.52202/075280-0178","pdf_url":null,"source":{"id":"https://openalex.org/S4363606243","display_name":"neural information processing systems","issn_l":"1049-5258","issn":["1049-5258"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","norway-2"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Advances in Neural Information Processing Systems 36","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2311.01118","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5051408309","display_name":"Mohammadamin Tavakoli","orcid":"https://orcid.org/0000-0002-8833-3891","observed_orcids":["https://orcid.org/0000-0002-8833-3891"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mohammadamin Tavakoli","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5127787031","display_name":"Pierre Baldi","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pierre Baldi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068803389","display_name":"Annmarie G. Carlton","orcid":"https://orcid.org/0000-0002-8574-1507","observed_orcids":["https://orcid.org/0000-0002-8574-1507"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ann Marie Carlton","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054501273","display_name":"Yin Ting T. Chiu","orcid":"https://orcid.org/0000-0001-5449-8391","observed_orcids":["https://orcid.org/0000-0001-5449-8391"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yin Ting Chiu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061083354","display_name":"Alexander Shmakov","orcid":"https://orcid.org/0000-0002-7440-9525","observed_orcids":["https://orcid.org/0000-0002-7440-9525"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Alexander Shmakov","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5081882130","display_name":"David L. Van Vranken","orcid":"https://orcid.org/0000-0001-5964-7042","observed_orcids":["https://orcid.org/0000-0001-5964-7042"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"David Van Vranken","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":9.0187,"has_fulltext":true,"cited_by_count":8,"citation_normalized_percentile":{"value":0.98031496,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":93,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"4080","last_page":"4096"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11948","display_name":"Machine Learning in Materials Science","score":0.48410001397132874,"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":0.48410001397132874,"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":0.3237000107765198,"subfield":{"id":"https://openalex.org/subfields/3002","display_name":"Drug Discovery"},"field":{"id":"https://openalex.org/fields/30","display_name":"Pharmacology, Toxicology and Pharmaceutics"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10002","display_name":"Advanced Chemical Physics Studies","score":0.04619999974966049,"subfield":{"id":"https://openalex.org/subfields/3107","display_name":"Atomic and Molecular Physics, and Optics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/radical-reactions","display_name":"radical reactions","score":0.7039999961853027},{"id":"https://openalex.org/keywords/reaction-mechanism-prediction","display_name":"reaction mechanism prediction","score":0.7300000190734863},{"id":"https://openalex.org/keywords/contrastive-learning","display_name":"contrastive learning","score":0.8899999856948853},{"id":"https://openalex.org/keywords/atmospheric-chemistry","display_name":"atmospheric chemistry","score":0.9470000267028809}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5497000217437744},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4814000129699707},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.43619999289512634},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.33059999346733093},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.3303000032901764},{"id":"https://openalex.org/C2777629044","wikidata":"https://www.wikidata.org/wiki/Q614959","display_name":"Contrastive analysis","level":2,"score":0.31369999051094055},{"id":"https://openalex.org/C2982736386","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Statistical learning","level":2,"score":0.30000001192092896},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.2802000045776367},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.26750001311302185},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2533000111579895}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.52202/075280-0178","doi":"https://doi.org/10.52202/075280-0178","is_oa":false,"landing_page_url":"https://doi.org/10.52202/075280-0178","pdf_url":null,"source":{"id":"https://openalex.org/S4363606243","display_name":"neural information processing systems","issn_l":"1049-5258","issn":["1049-5258"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","norway-2"],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Advances in Neural Information Processing Systems 36","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2311.01118","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2311.01118","pdf_url":"https://arxiv.org/pdf/2311.01118","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,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2311.01118","doi":"https://doi.org/10.48550/arxiv.2311.01118","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2311.01118","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,"listed_in":[],"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"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2311.01118","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2311.01118","pdf_url":"https://arxiv.org/pdf/2311.01118","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,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7133196486.pdf","grobid_xml":"https://content.openalex.org/works/W7133196486.grobid-xml"},"referenced_works_count":7,"referenced_works":["https://openalex.org/W2133564696","https://openalex.org/W2606780347","https://openalex.org/W2896457183","https://openalex.org/W2907182766","https://openalex.org/W3012984144","https://openalex.org/W3147020900","https://openalex.org/W4221161246"],"related_works":["https://openalex.org/W4406917942","https://openalex.org/W4414631654","https://openalex.org/W4415436131","https://openalex.org/W7014465539","https://openalex.org/W2066412916","https://openalex.org/W7067642792","https://openalex.org/W2283491960","https://openalex.org/W4416746231","https://openalex.org/W4403518786","https://openalex.org/W4414740162"],"abstract_inverted_index":{"Deep":[0],"learning-based":[1],"reaction":[2,48],"predictors":[3],"have":[4],"undergone":[5],"significant":[6],"architectural":[7],"evolution.":[8],"However,":[9],"their":[10],"reliance":[11],"on":[12],"reactions":[13],"from":[14],"the":[15,61,99,109],"US":[16],"Patent":[17],"Office":[18],"results":[19,107],"in":[20,56,113,128],"a":[21,46,91],"lack":[22],"of":[23,65,77,79,94,111,119],"interpretable":[24,63,117],"predictions":[25,118],"and":[26,37,84,116,122],"limited":[27],"generalization":[28],"capability":[29],"to":[30,97],"other":[31],"chemistry":[32],"domains,":[33],"such":[34],"as":[35],"radical":[36,71,95,104,120],"atmospheric":[38,129],"chemistry.":[39,130],"To":[40],"address":[41],"these":[42],"challenges,":[43],"we":[44],"introduce":[45],"new":[47],"predictor":[49],"system,":[50],"RMechRP,":[51],"that":[52],"leverages":[53],"contrastive":[54],"learning":[55],"conjunction":[57],"with":[58],"mechanistic":[59],"pathways,":[60],"most":[62],"representation":[64],"chemical":[66,80],"reactions.":[67,81,105],"Specifically":[68],"designed":[69],"for":[70,102,125],"reactions,":[72,96,121],"RMechRP":[73,112],"provides":[74],"different":[75],"levels":[76],"interpretation":[78],"We":[82],"develop":[83],"train":[85],"multiple":[86],"deep-learning":[87],"models":[88],"using":[89],"RMechDB,":[90],"public":[92],"database":[93],"establish":[98],"first":[100],"benchmark":[101],"predicting":[103],"Our":[106],"demonstrate":[108],"effectiveness":[110],"providing":[114],"accurate":[115],"its":[123],"potential":[124],"various":[126],"applications":[127]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":3}],"updated_date":"2026-11-13T13:42:25.865203","created_date":"2026-03-03T00:00:00"}
