{"id":"https://openalex.org/W4417292270","doi":"https://doi.org/10.48550/arxiv.2512.09757","title":"Circuits, Features, and Heuristics in Molecular Transformers","display_name":"Circuits, Features, and Heuristics in Molecular Transformers","publication_year":2025,"publication_date":"2025-12-10","ids":{"openalex":"https://openalex.org/W4417292270","doi":"https://doi.org/10.48550/arxiv.2512.09757"},"language":null,"primary_location":{"id":"pmh:oai:arXiv.org:2512.09757","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2512.09757","pdf_url":"https://arxiv.org/pdf/2512.09757","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2512.09757","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Varadi, Kristof","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Varadi, Kristof","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5105080755","display_name":"M\u00e1rk Marosi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Marosi, Mark","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5041069751","display_name":"P\u00e9ter Antal","orcid":"https://orcid.org/0000-0002-4370-2198"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Antal, Peter","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":true,"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":0.9215999841690063,"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":0.9215999841690063,"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":0.050599999725818634,"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/T11273","display_name":"Advanced Graph Neural Networks","score":0.009399999864399433,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/heuristics","display_name":"Heuristics","score":0.7369999885559082},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.6173999905586243},{"id":"https://openalex.org/keywords/parsing","display_name":"Parsing","score":0.5493000149726868},{"id":"https://openalex.org/keywords/computational-model","display_name":"Computational model","score":0.4602000117301941},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.4178999960422516},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.34549999237060547}],"concepts":[{"id":"https://openalex.org/C127705205","wikidata":"https://www.wikidata.org/wiki/Q5748245","display_name":"Heuristics","level":2,"score":0.7369999885559082},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7073000073432922},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.6173999905586243},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5953999757766724},{"id":"https://openalex.org/C186644900","wikidata":"https://www.wikidata.org/wiki/Q194152","display_name":"Parsing","level":2,"score":0.5493000149726868},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.49079999327659607},{"id":"https://openalex.org/C66024118","wikidata":"https://www.wikidata.org/wiki/Q1122506","display_name":"Computational model","level":2,"score":0.4602000117301941},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.4178999960422516},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.34549999237060547},{"id":"https://openalex.org/C124223222","wikidata":"https://www.wikidata.org/wiki/Q2281940","display_name":"Chemical process","level":2,"score":0.32179999351501465},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2955000102519989},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.2809000015258789},{"id":"https://openalex.org/C2776145971","wikidata":"https://www.wikidata.org/wiki/Q30673951","display_name":"Labeled data","level":2,"score":0.2799000144004822},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.26989999413490295},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.25130000710487366}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2512.09757","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2512.09757","pdf_url":"https://arxiv.org/pdf/2512.09757","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2512.09757","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2512.09757","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":"pmh:oai:arXiv.org:2512.09757","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2512.09757","pdf_url":"https://arxiv.org/pdf/2512.09757","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G6598701666","display_name":null,"funder_award_id":"1/2024","funder_id":"https://openalex.org/F4320320300","funder_display_name":"European Commission"}],"funders":[{"id":"https://openalex.org/F4320320300","display_name":"European Commission","ror":"https://ror.org/00k4n6c32"},{"id":"https://openalex.org/F4320328860","display_name":"Magyarorsz\u00e1g Korm\u00e1nya","ror":"https://ror.org/007ekx298"}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4417292270.pdf"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Transformers":[0],"generate":[1],"valid":[2],"and":[3,60,87],"diverse":[4],"chemical":[5,63],"structures,":[6],"but":[7],"little":[8],"is":[9],"known":[10],"about":[11],"the":[12,20,40],"mechanisms":[13],"that":[14,89],"enable":[15],"these":[16],"models":[17],"to":[18,38,94],"capture":[19],"rules":[21],"of":[22,30,49],"molecular":[23],"representation.":[24],"We":[25,51,80],"present":[26],"a":[27],"mechanistic":[28,90],"analysis":[29],"autoregressive":[31],"transformers":[32],"trained":[33],"on":[34,84],"drug-like":[35],"small":[36],"molecules":[37],"reveal":[39],"computational":[41,53],"structure":[42],"underlying":[43],"their":[44],"capabilities":[45],"across":[46],"multiple":[47],"levels":[48],"abstraction.":[50],"identify":[52],"patterns":[54],"consistent":[55],"with":[56,75],"low-level":[57],"syntactic":[58],"parsing":[59],"more":[61],"abstract":[62],"validity":[64],"constraints.":[65],"Using":[66],"sparse":[67],"autoencoders":[68],"(SAEs),":[69],"we":[70],"extract":[71],"feature":[72],"dictionaries":[73],"associated":[74],"chemically":[76],"relevant":[77],"activation":[78],"patterns.":[79],"validate":[81],"our":[82],"findings":[83],"downstream":[85],"tasks":[86],"find":[88],"insights":[91],"can":[92],"translate":[93],"predictive":[95],"performance":[96],"in":[97],"various":[98],"practical":[99],"settings.":[100]},"counts_by_year":[],"updated_date":"2026-07-03T08:13:44.112507","created_date":"2025-12-12T00:00:00"}
