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This reminds me of AlphaGo and AlphaZero. DeepMind was able to produce a very solid model on their first attempt, at both protein folding and at Go (and Starcra

This reminds me of AlphaGo and AlphaZero. DeepMind was able to produce a very solid model on their first attempt, at both protein folding and at Go (and Starcraft2 as well). Their second models, however, seemed to blow their first out of the water. This bodes extremely well for the future of computational biology, I'm very excited thinking about the prospects. If we know how a protein folds, we know its shape, meaning we know which shaped/charged molecules are needed to act as suppressors/enhancers of those proteins.

layer8 6y ago
One difference to AlphaZero though, if my understanding is correct, is that AlphaFold is trained on a predetermined data set and hence didn’t learn how “arbitrary” proteins fold in general, but just how the kinds of proteins fold for which we already know how they fold. To work more like AlphaZero, AlphaFold would have to be able to synthesize arbitrary proteins and run the experiments on them to verify and correct its predictions. Therefore it’s conceivable that AlphaFold is biased by the existing training data and doesn’t fully generalize to all proteins we would want to apply it to. Maybe that won’t be a problem in practice, but nevertheless it makes for a significant difference from what AlphaZero was about, being solely self-trained.
- the8472 6y ago
  > AlphaFold would have to be able to synthesize arbitrary proteins and run the experiments on them to verify and correct its predictions. Could this lead to a virtuous cycle where AlphaFold is used generate a ton of random sequences where it has low confidence, those are then screened for ease of synthesis, measured and the results used to improve the model? Edit: nevermind, according to another comment[0] there are still plenty of real proteins without experimental data left to explore. [0] https://news.ycombinator.com/item?id=25255601 https://news.ycombinator.com/item?id=25255601
- cma 6y ago
  > AlphaFold would have to be able to synthesize arbitrary proteins and run the experiments on them to verify and correct its predictions. It can verify how much it minimizes the potential energy, which may not always line up with how it would fold in the real world but is a strong indicator.

layer8 6y ago

One difference to AlphaZero though, if my understanding is correct, is that AlphaFold is trained on a predetermined data set and hence didn’t learn how “arbitrary” proteins fold in general, but just how the kinds of proteins fold for which we already know how they fold. To work more like AlphaZero, AlphaFold would have to be able to synthesize arbitrary proteins and run the experiments on them to verify and correct its predictions. Therefore it’s conceivable that AlphaFold is biased by the existing training data and doesn’t fully generalize to all proteins we would want to apply it to. Maybe that won’t be a problem in practice, but nevertheless it makes for a significant difference from what AlphaZero was about, being solely self-trained.

the8472 6y ago
> AlphaFold would have to be able to synthesize arbitrary proteins and run the experiments on them to verify and correct its predictions. Could this lead to a virtuous cycle where AlphaFold is used generate a ton of random sequences where it has low confidence, those are then screened for ease of synthesis, measured and the results used to improve the model? Edit: nevermind, according to another comment[0] there are still plenty of real proteins without experimental data left to explore. [0] https://news.ycombinator.com/item?id=25255601 https://news.ycombinator.com/item?id=25255601
cma 6y ago
> AlphaFold would have to be able to synthesize arbitrary proteins and run the experiments on them to verify and correct its predictions. It can verify how much it minimizes the potential energy, which may not always line up with how it would fold in the real world but is a strong indicator.