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  1. Proteins are essential to life. Predicting their 3D structure is a major unsolved challenge in biology and could impact disease understanding and drug discovery. I’m excited to announce that we have won the CASP13 protein folding competition!

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  2. Although neural networks usually require massive datasets to do impressive things, for me the highlight of is the fact that it achieved state-of-the-art using only 30K training examples. Code: Paper:

  3. 3. pro 2018.

    Using AI to help scientists solve big questions is at the core of DeepMind’s mission. Today we’re delighted to announce our first significant milestone: a successful application of machine learning to the protein folding problem. (1/5)

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  4. Thanks to the CASP community for organising such a great benchmark, the gold standard for assessing protein folding techniques. Congratulations to the Science Team at on this fantastic achievement! ( is listed as ‘A7D’)

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  5. Understanding how proteins fold is a fundamental scientific question that could one day help unlock treatments for a range of diseases. Excited that our work, which uses deep neural networks to predict protein structures, is published in today!

  6. 15. sij 2019.

    DeepMind's algorithm won a prominent protein folding competition last month, outperforming many well-known pharmaceutical companies. Its an amazing time to be alive! I'll explain how it works in this episode

  7. 17. sij

    Disappointed that didn’t get references quite right- first was

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  8. 15. sij

    Online : Improved protein structure prediction using potentials from deep learning

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  9. 3. pro 2018.
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  10. 4. velj

    Majority won. Covered wide range of applications of Evolutionary Couplings and new approach to protein design. And team now also reading

  11. 3. velj

    has released a python package of with for protein fold prediction: contact prediction network, associated model weights and ~40Gb CASP13 dataset. Paper:

  12. 31. sij

    Top 👍 has released a package implementation of which include the contact prediction network, associated model weights and CASP13 dataset as published in Nature ( Authors access link : )👨‍💻

  13. 26. sij

    Deep learning system "could model from scratch – i.e., based only on genetic sequence – better than any previous modelling system with a similar accuracy to systems drawing on templates of previously solved protein"

  14. 17. sij

    because didn’t credit should I

  15. Congrats to the team for publishing their exciting structure prediction work from CASP in and PROTEINS.

  16. 15. sij

    Two of our recent projects published in today! Our work on protein folding and another exploring the link between distributional RL and dopamine in the brain. Huge congrats to everyone who contributed to make these scientific achievements possible!

  17. 15. sij

    How do you predict "B" protein structure without any knowledge of "A" and with unprecedented "C" accuracy? Like this: by and their colleagues, deep neural net

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  18. 5. pro 2019.

    'nın ne kadar önemli bir güç olduğuna ve bilimin geleceğini etkileyecek en büyük potansiyellerden biri olduğuna dair bir kanıt daha: 🚀 Bu gelişmeyi Türkiye'de ilk kez yayınlayan 'de okumanın ayrıcalığını yaşayın.

  19. How is applying deep learning to the protein folding problem? AlphaFold team lead Andrew Senior recently came to tell us.

  20. Always a great pleasure to be back at , catching up with old friends, and giving a new talk on our latest work on , and . Sorry to those who didn’t get in to the lecture hall but the video is posted here:

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