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This reminds me of Google’s claim that another “AI” discovered millions of new materials. The results turned out to be a lot of useless noise but that was only

This reminds me of Google’s claim that another “AI” discovered millions of new materials. The results turned out to be a lot of useless noise but that was only apparent after actual expert spent hundreds of hours reviewed the results[0] 0: https://www.404media.co/google-says-it-discovered-millions-of-new-materials-with-ai-human-researchers/ https://www.404media.co/google-says-it-discovered-millions-o...

dekhn 2y ago
The alphafold work has been used across the industry (successfully, in the sense of blind prediction), and has been replicated independently. The work on alphafold will likely net Demis and John a Nobel prize in the next few years. (that said, one should always inspect Google publications with a fine-toothed comb and lots of skepticism, as they have a tendency to juice the results)
- 11101010001100 2y ago
  Depending on your expected value of quantum computing, the Nobel committee shouldn't wait too long.
  - dekhn 2y ago
    Personally I don't expect QC to be a competitor to ML in protein structure prediction for the foreseeable future. After spending more money on molecular dynamics than probably any other human being, I'm really skeptical that physical models of protein structures will compete with ML-based approaches (that exploit homology and other protein sequence similarities).
- nybsjytm 2y ago
  >The alphafold work has been used across the industry (successfully, in the sense of blind prediction), and has been replicated independently. This is clearly an overstatement, or at least very incomplete. See for instance https://www.nature.com/articles/s41592-023-02087-4 https://www.nature.com/articles/s41592-023-02087-4: "In many cases, AlphaFold predictions matched experimental maps remarkably closely. In other cases, even very high-confidence predictions differed from experimental maps on a global scale through distortion and domain orientation, and on a local scale in backbone and side-chain conformation. We suggest considering AlphaFold predictions as exceptionally useful hypotheses."
  - dekhn 2y ago
    Yep, I know Paul Adams (used to work with him at Berkeley Lab) and that's exactly the paper he'd publish. If you read that paper carefully (as we all have, since it's the strongest we've seen from the crystallography community so far) they're basically saying the results from AF are absolutely excellent, and fit for purpose. (put another way: if Paul publishes a paper saying your structure predictions have issues, and mostly finds tiny local issues and some distortion and domain orientation,r ather than absolutely incorrect fold prediction, it means your technique works really well, and people are just quibbling about details.)
    - nybsjytm 2y ago
      I don't know Paul Adams, so it's hard for me to know how to interpret your post. Is there anything else I can read that discusses the accuracy of AlphaFold?
      - dekhn 2y ago
        Yes, https://predictioncenter.org/casp15/ https://predictioncenter.org/casp15/ https://www.sciencedirect.com/science/article/pii/S0959440X23000684 https://www.sciencedirect.com/science/article/pii/S0959440X2... https://dasher.wustl.edu/bio5357/readings/oxford-alphafold2.pdf https://dasher.wustl.edu/bio5357/readings/oxford-alphafold2.... I can't find the link at the moment but from the perspective of the CASP leaders, AF2 was accurate enough that it's hard to even compare to the best structures determined experimentally, due to noise in the data/inadequacy of the metric. A number of crystallographers have also reported that the predictions helped them find errors in their own crystal-determined structures. If you're not really familiar enough with the field to understand the papers above, I recommend spending more time learning about the protein structure prediction problem, and how it relates to the epxerimental determination of structure using crystallography.
        nybsjytm 2y ago
        Thanks, those look helpful. Whenever I meet someone with relevant PhDs I ask their thoughts on AlphaFold, and I've gotten a wide variety of responses, from responses like yours to people who acknowledge its usefulness but are rather dismissive about its ultimate contribution.
Laaas 2y ago
> We have yet to find any strikingly novel compounds in the GNoME and Stable Structure listings, although we anticipate that there must be some among the 384,870 compositions. We also note that, while many of the new compositions are trivial adaptations of known materials, the computational approach delivers credible overall compositions, which gives us confidence that the underlying approach is sound. Doesn't seem outright useless.
deleted 2y ago
[deleted]

dekhn 2y ago

The alphafold work has been used across the industry (successfully, in the sense of blind prediction), and has been replicated independently. The work on alphafold will likely net Demis and John a Nobel prize in the next few years. (that said, one should always inspect Google publications with a fine-toothed comb and lots of skepticism, as they have a tendency to juice the results)

11101010001100 2y ago
Depending on your expected value of quantum computing, the Nobel committee shouldn't wait too long.
- dekhn 2y ago
  Personally I don't expect QC to be a competitor to ML in protein structure prediction for the foreseeable future. After spending more money on molecular dynamics than probably any other human being, I'm really skeptical that physical models of protein structures will compete with ML-based approaches (that exploit homology and other protein sequence similarities).
nybsjytm 2y ago
>The alphafold work has been used across the industry (successfully, in the sense of blind prediction), and has been replicated independently. This is clearly an overstatement, or at least very incomplete. See for instance https://www.nature.com/articles/s41592-023-02087-4 https://www.nature.com/articles/s41592-023-02087-4: "In many cases, AlphaFold predictions matched experimental maps remarkably closely. In other cases, even very high-confidence predictions differed from experimental maps on a global scale through distortion and domain orientation, and on a local scale in backbone and side-chain conformation. We suggest considering AlphaFold predictions as exceptionally useful hypotheses."
- dekhn 2y ago
  Yep, I know Paul Adams (used to work with him at Berkeley Lab) and that's exactly the paper he'd publish. If you read that paper carefully (as we all have, since it's the strongest we've seen from the crystallography community so far) they're basically saying the results from AF are absolutely excellent, and fit for purpose. (put another way: if Paul publishes a paper saying your structure predictions have issues, and mostly finds tiny local issues and some distortion and domain orientation,r ather than absolutely incorrect fold prediction, it means your technique works really well, and people are just quibbling about details.)
  - nybsjytm 2y ago
    I don't know Paul Adams, so it's hard for me to know how to interpret your post. Is there anything else I can read that discusses the accuracy of AlphaFold?
    - dekhn 2y ago
      Yes, https://predictioncenter.org/casp15/ https://predictioncenter.org/casp15/ https://www.sciencedirect.com/science/article/pii/S0959440X23000684 https://www.sciencedirect.com/science/article/pii/S0959440X2... https://dasher.wustl.edu/bio5357/readings/oxford-alphafold2.pdf https://dasher.wustl.edu/bio5357/readings/oxford-alphafold2.... I can't find the link at the moment but from the perspective of the CASP leaders, AF2 was accurate enough that it's hard to even compare to the best structures determined experimentally, due to noise in the data/inadequacy of the metric. A number of crystallographers have also reported that the predictions helped them find errors in their own crystal-determined structures. If you're not really familiar enough with the field to understand the papers above, I recommend spending more time learning about the protein structure prediction problem, and how it relates to the epxerimental determination of structure using crystallography.
      - nybsjytm 2y ago
        Thanks, those look helpful. Whenever I meet someone with relevant PhDs I ask their thoughts on AlphaFold, and I've gotten a wide variety of responses, from responses like yours to people who acknowledge its usefulness but are rather dismissive about its ultimate contribution.

Laaas 2y ago

> We have yet to find any strikingly novel compounds in the GNoME and Stable Structure listings, although we anticipate that there must be some among the 384,870 compositions. We also note that, while many of the new compositions are trivial adaptations of known materials, the computational approach delivers credible overall compositions, which gives us confidence that the underlying approach is sound. Doesn't seem outright useless.

deleted 2y ago

[deleted]