{"id":"https://openalex.org/W7165398286","doi":"https://doi.org/10.48550/arxiv.2606.19377","title":"Emyx: Fast and efficient all-atom protein generation","display_name":"Emyx: Fast and efficient all-atom protein generation","publication_year":2026,"publication_date":"2026-06-12","ids":{"openalex":"https://openalex.org/W7165398286","doi":"https://doi.org/10.48550/arxiv.2606.19377"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2606.19377","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2606.19377","pdf_url":"https://arxiv.org/pdf/2606.19377","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"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2606.19377","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5001148647","display_name":"Nicholas H. Williams","orcid":"https://orcid.org/0000-0002-4457-4220","observed_orcids":["https://orcid.org/0000-0002-4457-4220"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Williams, Nicholas J.","raw_affiliation_strings":["Xyme , Oxford , UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xyme , Oxford , UK","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114389198","display_name":"Ward Haddadin","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Haddadin, Ward","raw_affiliation_strings":["Xyme , Oxford , UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xyme , Oxford , UK","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089988446","display_name":"Matteo Paolo Ferla","orcid":"https://orcid.org/0000-0002-5508-4673","observed_orcids":["https://orcid.org/0000-0002-5508-4673"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ferla, Matteo P.","raw_affiliation_strings":["Xyme , Oxford , UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xyme , Oxford , UK","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091100763","display_name":"Constantin Schneider","orcid":"https://orcid.org/0000-0002-2579-0307","observed_orcids":["https://orcid.org/0000-0002-2579-0307"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Schneider, Constantin","raw_affiliation_strings":["Xyme , Oxford , UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xyme , Oxford , UK","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054390969","display_name":"Nicholas B. Woodall","orcid":"https://orcid.org/0000-0002-2423-6590","observed_orcids":["https://orcid.org/0000-0002-2423-6590"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Woodall, Nicholas B.","raw_affiliation_strings":["Xyme , Oxford , UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xyme , Oxford , UK","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089206130","display_name":"Ruby Sedgwick","orcid":"https://orcid.org/0000-0001-8267-7995","observed_orcids":["https://orcid.org/0000-0001-8267-7995"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sedgwick, Ruby","raw_affiliation_strings":["Xyme , Oxford , UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xyme , Oxford , UK","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5075251844","display_name":"Christian Degnbol Madsen","orcid":"https://orcid.org/0000-0001-8218-7160","observed_orcids":["https://orcid.org/0000-0001-8218-7160"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Madsen, Christian D.","raw_affiliation_strings":["Xyme , Oxford , UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xyme , Oxford , UK","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076759117","display_name":"Andrew L. Hopkins","orcid":"https://orcid.org/0000-0003-1977-018X","observed_orcids":["https://orcid.org/0000-0003-1977-018X"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hopkins, Andrew L.","raw_affiliation_strings":["Xyme , Oxford , UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xyme , Oxford , UK","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071950872","display_name":"Douglas E. V. Pires","orcid":"https://orcid.org/0000-0002-3004-2119","observed_orcids":["https://orcid.org/0000-0002-3004-2119","https://orcid.org/0000-0003-1859-177X"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pires, Douglas E. V.","raw_affiliation_strings":["Xyme , Oxford , UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xyme , Oxford , UK","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084050064","display_name":"Edward O. Pyzer\u2010Knapp","orcid":"https://orcid.org/0000-0002-8232-8282","observed_orcids":["https://orcid.org/0000-0002-8232-8282"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pyzer-Knapp, Edward O.","raw_affiliation_strings":["Xyme , Oxford , UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xyme , Oxford , UK","institution_ids":[]}]}],"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":1,"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/T10044","display_name":"Protein Structure and Dynamics","score":0.9907000064849854,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular 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"}},"topics":[{"id":"https://openalex.org/T10044","display_name":"Protein Structure and Dynamics","score":0.9907000064849854,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular 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"}},{"id":"https://openalex.org/T10211","display_name":"Computational Drug Discovery Methods","score":0.0026000000070780516,"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/T12254","display_name":"Machine Learning in Bioinformatics","score":0.0020000000949949026,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular 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/protein-design","display_name":"protein design","score":0.5709999799728394},{"id":"https://openalex.org/keywords/enzyme-design","display_name":"enzyme design","score":0.6380000114440918},{"id":"https://openalex.org/keywords/conditional-flow-matching","display_name":"conditional flow matching","score":0.8730000257492065},{"id":"https://openalex.org/keywords/flow-matching","display_name":"flow matching","score":0.3790000081062317},{"id":"https://openalex.org/keywords/diffusion-models","display_name":"diffusion models","score":0.531000018119812},{"id":"https://openalex.org/keywords/scaffold-diversity","display_name":"scaffold diversity","score":0.7799999713897705},{"id":"https://openalex.org/keywords/structural-novelty","display_name":"structural novelty","score":0.4519999921321869}],"concepts":[{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.6078000068664551},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.552299976348877},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5286999940872192},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4970000088214874},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.48579999804496765},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.4551999866962433},{"id":"https://openalex.org/C174348530","wikidata":"https://www.wikidata.org/wiki/Q188635","display_name":"Bridging (networking)","level":2,"score":0.448199987411499},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.3666999936103821},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.35910001397132874},{"id":"https://openalex.org/C179799912","wikidata":"https://www.wikidata.org/wiki/Q205084","display_name":"Computational complexity theory","level":2,"score":0.3343000113964081},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.33329999446868896},{"id":"https://openalex.org/C38349280","wikidata":"https://www.wikidata.org/wiki/Q1434290","display_name":"Flow (mathematics)","level":2,"score":0.3271999955177307},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.32510000467300415},{"id":"https://openalex.org/C147764199","wikidata":"https://www.wikidata.org/wiki/Q6865248","display_name":"Minification","level":2,"score":0.2915000021457672},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.289000004529953},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.2736999988555908},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.26649999618530273},{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.26420000195503235},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.2540000081062317}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2606.19377","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2606.19377","pdf_url":"https://arxiv.org/pdf/2606.19377","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.2606.19377","doi":"https://doi.org/10.48550/arxiv.2606.19377","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.19377","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:2606.19377","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2606.19377","pdf_url":"https://arxiv.org/pdf/2606.19377","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/W7165398286.pdf","grobid_xml":"https://content.openalex.org/works/W7165398286.grobid-xml"},"referenced_works_count":15,"referenced_works":["https://openalex.org/W2103385859","https://openalex.org/W3006439205","https://openalex.org/W4296032638","https://openalex.org/W4321480048","https://openalex.org/W4327550249","https://openalex.org/W4396721167","https://openalex.org/W4403324307","https://openalex.org/W4403882592","https://openalex.org/W4406440058","https://openalex.org/W4408934460","https://openalex.org/W4411463597","https://openalex.org/W4414313157","https://openalex.org/W4414696188","https://openalex.org/W4416774217","https://openalex.org/W7138160709"],"related_works":["https://openalex.org/W7163811219","https://openalex.org/W4387595626","https://openalex.org/W4416955829","https://openalex.org/W4415082968","https://openalex.org/W7102680813","https://openalex.org/W7164881729","https://openalex.org/W4399270588","https://openalex.org/W7214951716","https://openalex.org/W7203571009","https://openalex.org/W4414975405"],"abstract_inverted_index":{"Computational":[0],"enzyme":[1,138],"design":[2,139],"requires":[3],"generating":[4],"proteins":[5],"that":[6,14,47,75],"scaffold":[7,158],"catalytic":[8,153],"residues":[9],"and":[10,19,41,90,133,152,160],"ligands,":[11],"a":[12,69],"task":[13],"demands":[15],"both":[16,131,148],"geometric":[17,60,161],"accuracy":[18],"structural":[20,156],"diversity":[21],"from":[22,33],"the":[23,100,105,126,136],"underlying":[24],"generative":[25],"model.":[26],"Current":[27],"all-atom":[28],"generators":[29],"inherit":[30],"expensive":[31],"architectures":[32],"structure":[34],"prediction,":[35],"leading":[36],"to":[37],"high":[38],"training":[39,112,164],"costs":[40],"limited":[42],"sample":[43],"diversity.":[44],"We":[45,93],"argue":[46],"much":[48],"of":[49,99],"this":[50],"complexity":[51],"is":[52,68],"unnecessary":[53],"for":[54,119],"generators,":[55],"which":[56],"condition":[57],"on":[58],"sparse":[59,91],"constraints":[61],"rather":[62],"than":[63,172],"rich":[64],"co-evolutionary":[65],"signals.":[66],"Emyx":[67,129],"140M-parameter":[70],"conditional":[71,88],"flow":[72,101,110],"matching":[73,102,111],"model":[74],"concentrates":[76],"capacity":[77],"within":[78],"standard":[79],"transformer":[80],"blocks,":[81],"replacing":[82],"heavy":[83],"embedding":[84],"stacks":[85],"with":[86,114],"lightweight":[87],"representations":[89],"connectivity.":[92],"additionally":[94],"derive":[95],"an":[96],"exact":[97],"reparametrisation":[98],"interpolant":[103],"into":[104],"EDM":[106],"noise-level":[107],"framework,":[108],"bridging":[109],"efficiency":[113],"state-of-the-art":[115],"sampling":[116],"methods":[117],"designed":[118],"diffusion":[120],"models":[121],"without":[122],"retraining.":[123],"Despite":[124],"being":[125],"smallest":[127],"model,":[128],"outperforms":[130],"Prote\u00edna-Complexa":[132],"RFdiffusion3":[134],"against":[135],"AME":[137],"benchmark":[140],"across":[141],"success":[142],"rate":[143],"under":[144],"strict":[145],"evaluation":[146],"requiring":[147],"global":[149],"fold":[150],"recovery":[151],"geometry":[154],"accuracy,":[155],"novelty,":[157],"diversity,":[159],"validity,":[162],"while":[163],"in":[165],"just":[166],"$682$":[167],"GPU-hours,":[168],"roughly":[169],"$4\\times$":[170],"less":[171],"RFdiffusion3.":[173]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2026-06-20T00:00:00"}
