"AlphaFold achieves a median score of 87.0 GDT". Game changing, and a huge improvement, but not 100% solved. Also this is for static folding. Dynamic folding and interaction is a much harder problem. Those need to be tackled too before I would consider protein folding 'solved'.
They solved the latest folding competition benchmark set.
Shorter problems are easy to solve. Median score is mix of easier hand harder problems. Next year competition will have new set of much bigger and harder problems to solve.
This seems like a leap, not solved as in having solution that just works and scales.
It's probably never going to be solved though right. To truly solve protein folding we'd have to have a program that can stimulate a small but still significant system at the QM level; looks like deep learning can get us 60% (conservatively estimating the whole problem domain ) but not all the edge cases, just like it did in other problem domains as well.
Despite this breakthrough by DeepMind, at this point we still do not understand protein folding. That makes it very hard to say precisely which features would be required to do the simulation correctly.
DeepMind/AlphaFold might have something to contribute there too, depending on how interpretable their network model(s?) are.
They seem to have a completely new tension algorithm that's doing the heavy lifting now, so it's likely we will learn much about how folding practically works from these results as well.
It remains unclear whether QM is required to fold proteins accurately. So far classical methods have shown they require far less computer power to get far closer to the right structure.
'Never' is a long timespan :) It will be solved, sooner or later. The universe will be fully understood and manipulated. By us, a modified version of us, or some other entity, perhaps even one we created. 300 years ago 'electricity' wasn't even a word. We can imagine what 500 years into the future will be, with an exponentially more advanced tech, worse than a caveman could imagine the concept of 'machine learning'.
>Those need to be tackled too before I would consider protein folding 'solved'
Semantics. From a systemtheoretical point of view, dynamic folding is an abstraction of static folding; solve (i.e. understand the underlying mechanisms) static folding and you can start progressing on dynamic folding, building up on your previously achieved solution.
Wether it's solved or not depends on wether you mean `general folding` or the `entire spectrum of folding` when considering the problem.
My intuition for deeplearning was exactly that, statistical inference of underlying mechanisms. But I haven't read the paper yet, so you might be right
