4 ms·

If I am not wrong, some of the probelms AlphaFold can solve are quite limited. Even if the results are promising, protein folding is an insanely complicated pro

If I am not wrong, some of the probelms AlphaFold can solve are quite limited. Even if the results are promising, protein folding is an insanely complicated problem and having tools like alphaFold be good at some aspects of it can be useful but cannot crack the bigger nut by design as far as I am aware. It currently performs ok on protein prediction but cannot do new proteins, which would theoretically be possible with quantum computers. And its not just a matter of giving alphafold more resources they are entirely different problems

xiphias2 4y ago
I don't totally understand your point. AlphaFold is great at protein folding, and the main limitation is predicting protein structures with multiple states when protein interactions happen. That's the next task the team is working on (it's the natural next step for biology simulation), and if the team is successful, it will be incredibly valuable (it can easily be the next multibillion dollar business for Alphabet, and a huge improvement in how drug research is done. I don't see quantum computers anywhere near as close to solving a real problem with drug research as AlphaFold (although I don't argue with the theoritical possibility in the future, but not in the next 5-10 years, where AlphaFold can shine).
- Arkhaine_kupo 4y ago
  I apologise as my field is not related to proteins, but I do recall an interesting read about the subject from a few months ago. Here is the link if you wanna follow it yourself. https://news.ycombinator.com/item?id=32262856 https://news.ycombinator.com/item?id=32262856 There are some interesting criticisms from the language, to the results, to the limitations of AlphaFold that I think seem to represent the prevailing opinion from biologists about the current scope of what ML can achieve on their field. There is also push back to the complaints and some very optimistic people about Deep learning models on the field so perhaps you are right.

xiphias2 4y ago

I don't totally understand your point. AlphaFold is great at protein folding, and the main limitation is predicting protein structures with multiple states when protein interactions happen. That's the next task the team is working on (it's the natural next step for biology simulation), and if the team is successful, it will be incredibly valuable (it can easily be the next multibillion dollar business for Alphabet, and a huge improvement in how drug research is done. I don't see quantum computers anywhere near as close to solving a real problem with drug research as AlphaFold (although I don't argue with the theoritical possibility in the future, but not in the next 5-10 years, where AlphaFold can shine).

Arkhaine_kupo 4y ago
I apologise as my field is not related to proteins, but I do recall an interesting read about the subject from a few months ago. Here is the link if you wanna follow it yourself. https://news.ycombinator.com/item?id=32262856 https://news.ycombinator.com/item?id=32262856 There are some interesting criticisms from the language, to the results, to the limitations of AlphaFold that I think seem to represent the prevailing opinion from biologists about the current scope of what ML can achieve on their field. There is also push back to the complaints and some very optimistic people about Deep learning models on the field so perhaps you are right.