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It's an improvement- and a big one- but not a solution to the problem. It mainly shows just how stuck the community had gotten with their techniques and how recently improvements in DNNs and information theory methods can be exploited if you have lots of TPU time.
It’s officially recognized as a solution.
Not knowing a lot about biotechnology, I read the article and it sounds great, but how big is this as a gamechanger? Can someone comment on how big are the implications of this in, let’s say, 5 years from now, on day to day life? Does this mean that biotech is going to explode? Or just that drugs will come to market faster, perhaps cheaper for rare diseases, but from the same industry structure as always?
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100% accuracy is "solved".
Solving the inverse problem would be even more valuable -- given a specific shape (and other biochemical desiderata), what sequence of amino acids would create that protein? As hard as the protein folding problem is, the inverse problem is harder still. THAT is the one true grail.
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I agree. "AlphaFold achieves a median score of 87.0 GDT". While this is a major advance, to me 100 GDT would be 'solved', not 87.
By this metric, nothing has been ever solved in natural sciences. So this is not a useful metric.
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We’d have to see the distribution of GDT scores evaluated on unknown proteins to say anything about how confident we can be. If the distribution is tightly distributed around the median then great, this works really well. If the variance is large though then you’re going to have a hard time using this for meaningful predictions.
According to the article there's a confidence score as well. As long as this is sufficiently predictive of errors either a tight or wide distribution is likely acceptable.
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Far from an expert on complexity theory, but NP-hard problems can be approximated in polynomial time. With Deep Learning you are doing approximation. So this is nothing ground breaking in that respect.
That actually isn't totally true. Approximate methods, in the formal sense, require a guarantee that they perform within X of the optimal solution. Not all NP-hard problems have polynomial approximations and the methods shown here are likely not approximations because they very likely provide no guarantees on performance. They provide zero guarantees.
"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'.
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.
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Not to mention the fact that two years ago they took it from 45% to >60%. If they can continue improving, even with an exponential decay in rate of improvement, this is certainly a stunning example of technological disruption.
Even without any improvement, the amount of grunt-work the AI can pre-do and get down to a short-list - that in itself will see changes in progress speeding research up.
There's no reason to believe the list will contain all solutions, however.
Title as submitted is hyperbole, please fix?
"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'.
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.