Deep Learning with Proteins
📰 Hugging Face Blog
Deep learning with proteins combines biology and machine learning to analyze protein structures and functions
Action Steps
- Learn the basics of protein biology and machine learning
- Explore transfer learning and its applications in protein analysis
- Fine-tune protein language models using PyTorch or other frameworks
- Apply machine learning techniques to protein data for predictive modeling and analysis
Who Needs to Know This
Biologists and machine learning engineers can benefit from this intersection of fields to develop new models and applications
Key Insight
💡 Transfer learning is a critical breakthrough in applying machine learning to protein analysis
Share This
🧬💻 Deep learning with proteins: where biology meets machine learning #proteins #machinelearning
Key Takeaways
Deep learning with proteins combines biology and machine learning to analyze protein structures and functions
Full Article
Published Time: 2022-12-02T00:00:00.156Z
# Deep Learning with Proteins
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# [](https://huggingface.co/blog/deep-learning-with-proteins#deep-learning-with-proteins) Deep Learning With Proteins
Published December 2, 2022
[Update on GitHub](https://github.com/huggingface/blog/blob/main/deep-learning-with-proteins.md)
[- [x] Upvote 28](https://huggingface.co/login?next=%2Fblog%2Fdeep-learning-with-proteins)
* [](https://huggingface.co/Changjiang "Changjiang")
* [](https://huggingface.co/GVR "GVR")
* [](https://huggingface.co/Charvi "Charvi")
* [](https://huggingface.co/CwilsonPrime "CwilsonPrime")
* [](https://huggingface.co/TheEeeeLin "TheEeeeLin")
* [](https://huggingface.co/a0308 "a0308")
* +22
[](https://huggingface.co/Rocketknight1)
[Matthew Carrigan Rocketknight1 Follow](https://huggingface.co/Rocketknight1)
* [Introduction for biologists: What the hell is a language model?](https://huggingface.co/blog/deep-learning-with-proteins#introduction-for-biologists-what-the-hell-is-a-language-model "Introduction for biologists: What the hell is a language model?")
* [The critical breakthrough: Transfer learning](https://huggingface.co/blog/deep-learning-with-proteins#the-critical-breakthrough-transfer-learning "The critical breakthrough: Transfer learning")
* [Introduction for machine learning people: What the hell is a protein?](https://huggingface.co/blog/deep-learning-with-proteins#introduction-for-machine-learning-people-what-the-hell-is-a-protein "Introduction for machine learning people: What the hell is a protein?")
* [Bringing it together: Machine learning with proteins](https://huggingface.co/blog/deep-learning-with-proteins#bringing-it-together-machine-learning-with-proteins "Bringing it together: Machine learning with proteins")
* [Sounds cool, but I don’t know where to start!](https://huggingface.co/blog/deep-learning-with-proteins#sounds-cool-but-i-dont-know-where-to-start "Sounds cool, but I don’t know where to start!")
* [Conclusion](https://huggingface.co/blog/deep-learning-with-proteins#conclusion "Conclusion")
I have two audiences in mind while writing this. One is biologists who are trying to get into machine learning, and the other is machine learners who are trying to get into biology. If you’re not familiar with either biology or machine learning then you’re still welcome to come along, but you might find it a bit confusing at times! And if you’re already familiar with both, then you probably don’t need this post at all - you can just skip straight to our example notebooks to see these models in action:
* Fine-tuning protein language models ([PyTorch](https://colab.research.google.com/github/
# Deep Learning with Proteins
[Hugging Face](https://huggingface.co/)
* [Models](https://huggingface.co/models)
* [Datasets](https://huggingface.co/datasets)
* [Spaces](https://huggingface.co/spaces)
* [Buckets new](https://huggingface.co/storage)
* [Docs](https://huggingface.co/docs)
* [Enterprise](https://huggingface.co/enterprise)
* [Pricing](https://huggingface.co/pricing)
*
*
* * *
* [Log In](https://huggingface.co/login)
* [Sign Up](https://huggingface.co/join)
[Back to Articles](https://huggingface.co/blog)
# [](https://huggingface.co/blog/deep-learning-with-proteins#deep-learning-with-proteins) Deep Learning With Proteins
Published December 2, 2022
[Update on GitHub](https://github.com/huggingface/blog/blob/main/deep-learning-with-proteins.md)
[- [x] Upvote 28](https://huggingface.co/login?next=%2Fblog%2Fdeep-learning-with-proteins)
* [](https://huggingface.co/Changjiang "Changjiang")
* [](https://huggingface.co/GVR "GVR")
* [](https://huggingface.co/Charvi "Charvi")
* [](https://huggingface.co/CwilsonPrime "CwilsonPrime")
* [](https://huggingface.co/TheEeeeLin "TheEeeeLin")
* [](https://huggingface.co/a0308 "a0308")
* +22
[](https://huggingface.co/Rocketknight1)
[Matthew Carrigan Rocketknight1 Follow](https://huggingface.co/Rocketknight1)
* [Introduction for biologists: What the hell is a language model?](https://huggingface.co/blog/deep-learning-with-proteins#introduction-for-biologists-what-the-hell-is-a-language-model "Introduction for biologists: What the hell is a language model?")
* [The critical breakthrough: Transfer learning](https://huggingface.co/blog/deep-learning-with-proteins#the-critical-breakthrough-transfer-learning "The critical breakthrough: Transfer learning")
* [Introduction for machine learning people: What the hell is a protein?](https://huggingface.co/blog/deep-learning-with-proteins#introduction-for-machine-learning-people-what-the-hell-is-a-protein "Introduction for machine learning people: What the hell is a protein?")
* [Bringing it together: Machine learning with proteins](https://huggingface.co/blog/deep-learning-with-proteins#bringing-it-together-machine-learning-with-proteins "Bringing it together: Machine learning with proteins")
* [Sounds cool, but I don’t know where to start!](https://huggingface.co/blog/deep-learning-with-proteins#sounds-cool-but-i-dont-know-where-to-start "Sounds cool, but I don’t know where to start!")
* [Conclusion](https://huggingface.co/blog/deep-learning-with-proteins#conclusion "Conclusion")
I have two audiences in mind while writing this. One is biologists who are trying to get into machine learning, and the other is machine learners who are trying to get into biology. If you’re not familiar with either biology or machine learning then you’re still welcome to come along, but you might find it a bit confusing at times! And if you’re already familiar with both, then you probably don’t need this post at all - you can just skip straight to our example notebooks to see these models in action:
* Fine-tuning protein language models ([PyTorch](https://colab.research.google.com/github/
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