{"id":"https://openalex.org/W4226254615","doi":"https://doi.org/10.48550/arxiv.2203.04698","title":"Score-Based Generative Models for Molecule Generation","display_name":"Score-Based Generative Models for Molecule Generation","publication_year":2022,"publication_date":"2022-03-07","ids":{"openalex":"https://openalex.org/W4226254615","doi":"https://doi.org/10.48550/arxiv.2203.04698"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2203.04698","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2203.04698","pdf_url":"https://arxiv.org/pdf/2203.04698","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2203.04698","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5063596082","display_name":"Dwaraknath Gnaneshwar","orcid":"https://orcid.org/0000-0002-3771-8433"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Gnaneshwar, Dwaraknath","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039641760","display_name":"Bharath Ramsundar","orcid":"https://orcid.org/0000-0001-8450-4262"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ramsundar, Bharath","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051208390","display_name":"Dhairya Gandhi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gandhi, Dhairya","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065146210","display_name":"Rachel C. Kurchin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kurchin, Rachel","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5081036535","display_name":"Venkatasubramanian Viswanathan","orcid":"https://orcid.org/0000-0003-1060-5495"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Viswanathan, Venkatasubramanian","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5063596082"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":7,"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":0.9972000122070312,"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":0.9972000122070312,"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/T12859","display_name":"Cell Image Analysis Techniques","score":0.9883000254631042,"subfield":{"id":"https://openalex.org/subfields/1304","display_name":"Biophysics"},"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"}},{"id":"https://openalex.org/T11986","display_name":"Scientific Computing and Data Management","score":0.980400025844574,"subfield":{"id":"https://openalex.org/subfields/1802","display_name":"Information Systems and Management"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/generative-grammar","display_name":"Generative grammar","score":0.7053090929985046},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.615676760673523},{"id":"https://openalex.org/keywords/benchmarking","display_name":"Benchmarking","score":0.5635663270950317},{"id":"https://openalex.org/keywords/generative-model","display_name":"Generative model","score":0.5019147396087646},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.46671029925346375},{"id":"https://openalex.org/keywords/function","display_name":"Function (biology)","score":0.4535438120365143},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.43153202533721924},{"id":"https://openalex.org/keywords/score","display_name":"Score","score":0.4262322783470154},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4217792749404907},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.11609905958175659}],"concepts":[{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.7053090929985046},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.615676760673523},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.5635663270950317},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.5019147396087646},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.46671029925346375},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.4535438120365143},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.43153202533721924},{"id":"https://openalex.org/C65660741","wikidata":"https://www.wikidata.org/wiki/Q3952743","display_name":"Score","level":2,"score":0.4262322783470154},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4217792749404907},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.11609905958175659},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C78458016","wikidata":"https://www.wikidata.org/wiki/Q840400","display_name":"Evolutionary biology","level":1,"score":0.0},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0},{"id":"https://openalex.org/C162853370","wikidata":"https://www.wikidata.org/wiki/Q39809","display_name":"Marketing","level":1,"score":0.0},{"id":"https://openalex.org/C144133560","wikidata":"https://www.wikidata.org/wiki/Q4830453","display_name":"Business","level":0,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2203.04698","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2203.04698","pdf_url":"https://arxiv.org/pdf/2203.04698","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.2203.04698","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2203.04698","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":"pmh:oai:arXiv.org:2203.04698","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2203.04698","pdf_url":"https://arxiv.org/pdf/2203.04698","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4365211920","https://openalex.org/W3014948380","https://openalex.org/W4380551139","https://openalex.org/W4317695495","https://openalex.org/W4387506531","https://openalex.org/W4238433571","https://openalex.org/W3174044702","https://openalex.org/W2967848559","https://openalex.org/W4299831724","https://openalex.org/W4283803360"],"abstract_inverted_index":{"Recent":[0],"advances":[1],"in":[2,86],"generative":[3,18,40,79],"models":[4,19,41,80,118],"have":[5],"made":[6],"exploring":[7],"design":[8],"spaces":[9],"easier":[10],"for":[11,119],"de":[12],"novo":[13],"molecule":[14,87,120],"generation.":[15,121],"However,":[16],"popular":[17],"like":[20],"GANs":[21],"and":[22,35,67,142],"normalizing":[23],"flows":[24],"face":[25],"challenges":[26,44],"such":[27,94],"as":[28,59,95],"training":[29,34],"instabilities":[30],"due":[31,89],"to":[32,61,90,148],"adversarial":[33],"architectural":[36,92],"constraints,":[37],"respectively.":[38],"Score-based":[39],"sidestep":[42],"these":[43],"by":[45,112],"modelling":[46,62],"the":[47,50,63,97,110,114,139,144,150],"gradient":[48],"of":[49,116,134,156],"log":[51],"probability":[52],"density":[53,64],"using":[54,71],"a":[55,124,154],"score":[56,98,126],"function":[57,65,99,127],"approximation,":[58],"opposed":[60],"directly,":[66],"sampling":[68],"from":[69,138],"it":[70],"annealed":[72],"Langevin":[73],"Dynamics.":[74],"We":[75,122],"believe":[76],"that":[77],"score-based":[78,117],"could":[81],"open":[82],"up":[83],"new":[84],"opportunities":[85],"generation":[88],"their":[91],"flexibility,":[93],"replacing":[96],"with":[100],"an":[101],"SE(3)":[102],"equivariant":[103],"model.":[104],"In":[105],"this":[106],"work,":[107],"we":[108],"lay":[109],"foundations":[111],"testing":[113],"efficacy":[115],"train":[123],"Transformer-based":[125],"on":[128,153],"Self-Referencing":[129],"Embedded":[130],"Strings":[131],"(SELFIES)":[132],"representations":[133],"1.5":[135],"million":[136],"samples":[137,152],"ZINC":[140],"dataset":[141],"use":[143],"Moses":[145],"benchmarking":[146],"framework":[147],"evaluate":[149],"generated":[151],"suite":[155],"metrics.":[157]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":2}],"updated_date":"2026-02-09T09:26:11.010843","created_date":"2025-10-10T00:00:00"}