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.
back
2 comments
> 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.
> 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.