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Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences

Alexander Rives, Joshua Meier, Tom Sercu, Siddharth Goyal, Zeming Lin, Jason Liu, Demi Guo, Myle Ott, C. Lawrence Zitnick, Jerry Ma, Rob Fergus
doi: https://doi.org/10.1101/622803

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doi 
https://doi.org/10.1101/622803
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  • December 15, 2020.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.

Author Information

  1. Alexander Rives*,1,2,+,
  2. Joshua Meier*,1,
  3. Tom Sercu*,1,
  4. Siddharth Goyal*,1,
  5. Zeming Lin2,
  6. Jason Liu1,
  7. Demi Guo3,†,
  8. Myle Ott1,
  9. C. Lawrence Zitnick1,
  10. Jerry Ma4,† and
  11. Rob Fergus2
  1. 1Facebook AI Research
  2. 2Dept. of Computer Science, New York University
  3. 3Harvard University
  4. 4Booth School of Business, University of Chicago & Yale Law School
  1. ↵+Correspondence to: Alexander Rives <arives{at}cs.nyu.edu>. Pre-trained models available at: <https://github.com/facebookresearch/esm>.

  1. ↵* Equal contribution

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Posted December 15, 2020.
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Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
Alexander Rives, Joshua Meier, Tom Sercu, Siddharth Goyal, Zeming Lin, Jason Liu, Demi Guo, Myle Ott, C. Lawrence Zitnick, Jerry Ma, Rob Fergus
bioRxiv 622803; doi: https://doi.org/10.1101/622803
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