{
 "id": "RosettaCommons/ProteinMPNN",
 "owner": "RosettaCommons",
 "name": "ProteinMPNN",
 "downloads": 870,
 "likes": 0,
 "last_modified": "2026-10-13T00:33:10.000Z",
 "stated_licence": "other",
 "stated_from": "tag",
 "licence_name": "rosetta-license-1.0",
 "licence_link": "LICENSE.md",
 "file_licence": null,
 "licence_files": [
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 ],
 "sources": [],
 "gated": false,
 "tasks": [],
 "citation": "@article{Dauparas2022,\n    title = {Robust deep learning–based protein sequence design using ProteinMPNN},\n    volume = {378},\n    ISSN = {1095-9203},\n    url = {http://dx.doi.org/10.1126/science.add2187},\n    DOI = {10.1126/science.add2187},\n    number = {6615},\n    journal = {Science},\n    publisher = {American Association for the Advancement of Science (AAAS)},\n    author = {Dauparas,  J. and Anishchenko,  I. and Bennett,  N. and Bai,  H. and Ragotte,  R. J. and Milles,  L. F. and Wicky,  B. I. M. and Courbet,  A. and de Haas,  R. J. and Bethel,  N. and Leung,  P. J. Y. and Huddy,  T. F. and Pellock,  S. and Tischer,  D. and Chan,  F. and Koepnick,  B. and Nguyen,  H. and Kang,  A. and Sankaran,  B. and Bera,  A. K. and King,  N. P. and Baker,  D.},\n    year = {2022},\n    month = oct,\n    pages = {49–56}\n    }",
 "signal": "unclear",
 "reason": "The checks could not identify a recognised licence ID in the dataset's metadata.",
 "revision": "a08c260499baaa29cab68c7ff9d00ee02fbffa22",
 "stated_evidence": "https://huggingface.co/datasets/RosettaCommons/ProteinMPNN/blob/a08c260499baaa29cab68c7ff9d00ee02fbffa22/README.md",
 "file_evidence": "https://huggingface.co/datasets/RosettaCommons/ProteinMPNN/blob/a08c260499baaa29cab68c7ff9d00ee02fbffa22/LICENSE.md",
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}