The aspect of this story that fascinates me is that one could argue that DeepMind has not, in fact, solved anything. An inscrutable, black-box ML model has. While such an algorithm could ideally predict protein folding in any case, it can't explain anything about why, and therefore can't really advance the science.
An analogy might be that if you trained an AI model on billiard balls, it could become really good at telling you where a ball will end up when you hit it, but it could never tell you that the reason is that f=m*a, meaning it will do nothing to advance the science.
We've known the "why" of protein folding for many years. You can plug your amino acid chain into NWChem and have it spit out the system hamiltonian. From there it's just using the Schrodinger equation to evolve the system in time. Good luck finding a computer that can do that before the universe goes dark, though.
I guess you're hoping for some higher-level heuristic that would let us skip a lot of the computation. Maybe it exists. That isn't how nature does it, though.
uhhhhh nobody has convincingly shown for sure that applying time-dependent schrodinger to a protein hamiltonian would solve the folding problem, even given infinite computer time.
(note that I am one of the few people who has tried using extremely large amounts of computer time and classical force fields)
Reading your other comments you say you have a PhD in biophysics so yeah you know a lot more about this than me, so I'm interested - are there reasons to believe it isn't just a matter of scale (obviously we are talking about exponential scaling here aka completely infeasible but you know)? The field of quantum computing has also tied a lot of its value to efficient hamiltonian simulation and what that can do for biology & material science.
I only mean that nobody has done the experiment (nor is it feasible to run). My base hypothesis is, given enough computer time and an accurate potential function, molecular dynamics indeed would solve the limited protein folding problem (small domains that are soluble in water), but I also think there must be a far simpler and less expensive way to do it (most likely using deep neural nets that incorporate a wide range of information).
That said I also believe that QC in principle could be a way to address this effectively as well, but I'm waiting until I see somebody demonstrate something interesting and useful before I get excited.
We already understand the physics and chemistry underlying protein folding (the why), but proteins are composed of so many building blocks that applying this understanding by brute force is woefully impractical.
By analogy, billiards is nothing but highschool physics but understanding highschool physics does not on its own make you a master billiards player able to sink any shot.
Painstaking experimental ascertainment of protein structure doesn't really tell anyone "why" it ended up that way either, but it's still been worth doing and advances science a great deal.
Right. The challenging thing about protein folding is that we've been able to probe protein folding in numerous ways but none of them actually give a direct picture of what the process of folding looks like. That is, the specific physical configuration trajectories/envelope followed by an ensemble of folding proteins.
Just because you don't understand exactly how something works, don't mean it's not useful. Someone who doesn't understand exactly how everything works, like how do bikes stay upright and how does gravity work, can still use the bike to acquire food or advance science.
>Just because you don't understand exactly how something works, don't mean it's not useful
The OP didn't say that it is not useful, what they implied was that it is not actually science, which is correct. Science is a system that produces and organises knowledge. Chomsky made this point many years ago in a similar debate in linguistics. Statistical learning might produce results, but it tells us virtually nothing about the underlying laws or structures that govern language use.
ML in its current from is effectively the modern version of behaviourism and will, or already does, suffer from the same issues.
An analogy might be that if you trained an AI model on billiard balls, it could become really good at telling you where a ball will end up when you hit it, but it could never tell you that the reason is that f=m*a, meaning it will do nothing to advance the science.