yangkky/Machine-learning-for-proteins

• highabout#1

  Update the 'About' description to emphasize its nature as a curated list

  Why:

  CURRENT

  Listing of papers about machine learning for proteins.

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  A public, collaborative, and curated list of papers on machine learning applications in protein science, categorized by application and model type.

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• highreadme#2

  Reposition the README's opening to clearly state it's a curated list

  Why:

  CURRENT

  ## Papers on machine learning for proteins
  
  ### Background
  
  We recently released a review of machine learning methods in protein engineering, but the field changes so fast and there are so many new papers that any static document will inevitably be missing important work. This format also allows us to broaden the scope beyond engineering-specific applications. We hope that this will be a useful resource for people interested in the field.
  
  To the best of our knowledge, this is the first public, collaborative list of machine learning papers on protein applications.

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  ## A Curated, Collaborative List of Papers on Machine Learning for Proteins
  
  ### Background
  
  This repository serves as a public, collaborative, and continuously updated list of machine learning papers applied to protein science. While we recently released a review of machine learning methods in protein engineering, the field changes so fast that any static document inevitably misses important work. This format allows us to broaden the scope beyond engineering-specific applications and provide a useful resource for people interested in the field.

  OpenDoneDismiss

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

• CATEGORY QUERY

  Where can I find a comprehensive list of machine learning papers for protein applications?

  you: not recommended
  AI recommended (in order):

  1. PubMed
  2. Google Scholar
  3. arXiv
  4. Bioinformatics
  5. Nature Methods
  6. Nature Biotechnology
  7. Nature Machine Intelligence
  8. Journal of Chemical Information and Modeling (JCIM)
  9. PLoS Computational Biology
  10. GitHub

  AI recommended 10 alternatives but never named yangkky/Machine-learning-for-proteins. This is the gap to close.

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• CATEGORY QUERY

  What are the latest machine learning techniques for protein engineering and variant prediction?

  you: not recommended
  AI recommended (in order):

  1. DeepSequence
  2. ProteinVAE
  3. AlphaFold
  4. RFdiffusion
  5. ESM-2
  6. ProtT5
  7. MSA Transformer
  8. DeepMind's AlphaFold-driven RL for protein design

  AI recommended 8 alternatives but never named yangkky/Machine-learning-for-proteins. This is the gap to close.

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• Metadata completeness

  warn

  Suggestion:

• README presence

  pass

• Compared to common alternatives in this category, what is the core differentiator of yangkky/Machine-learning-for-proteins?

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  ✓AI named yangkky/Machine-learning-for-proteins explicitly

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• If a team adopts yangkky/Machine-learning-for-proteins in production, what risks or prerequisites should they evaluate first?

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  ✓AI named yangkky/Machine-learning-for-proteins explicitly

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• In one sentence, what problem does the repo yangkky/Machine-learning-for-proteins solve, and who is the primary audience?

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  ⚠AI did not name yangkky/Machine-learning-for-proteins — likely talking about a different project

  AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?