Jeez. Clickbait title, tons of fallacy, like goalpost moving.

Some context on myself: I have 15+ years of postgraduate chemistry/molecular bio/biophysics/biochemistry experience, then quit to go into tech, worked for AI hardware and now AI-driven software startups, mostly as backend (not implementing ML models, but I know how to do that and have done some small models for myself). I'm a pessimist about this AI innovation cycle's prospects in general AI and in particular drug discovery.

Has protein folding been solved? Yes. As a practitioner, what I want to be able to do is pull a sequence that I've retrieved from DNA, drop it into a computer, and get the structure out. Intuitive insight can FOLLOW from those results. For example: In one of my projects, I was able to look at the structure (a homolog had been solved, so I just did a dumb alignment and threading), identify that it was acting as an NPN transistor (unpublished details), fix the electron flow through the enzyme and improve the yield (https://link.springer.com/article/10.1186/1754-1611-7-17). Later, I looked at the structure of the enzyme, modified the surface charge in one particular part of it, and improved electron throughput again (https://www.mdpi.com/1422-0067/16/1/2020). This was with primitive "protein folding through homology" tools, now there's an good chance I could do these sorts of things with proteins I don't have homologous structure for.

These are the sorts of things that protein folding enables. One more thought -- I bet that DeepMind can do things like make it obvious where there are certain posttranslation sites (like FeS clusters) or make it obvious where there is a cryptic phosphorylation or glycosylation site (sequence holdover from a previous mutant that no longer has its expected functionality) because it's been buried.

Will there be corner cases? Yes. Probably deepmind will have difficulty solving the fold of amyloid fibrils. Probably deepmind will have some difficulty with super-strange post-translational modifications (think things like GFP's core fluorophore), or if you design a drug where you splice in an unnatural or D-amino acid, or oddities like that.

> we are not at the point where this AI tool can be used for drug discovery

I disagree. Sure, it probably won't be able to find a small molecule binding site. It almost certainly won't be able to design a drug that has a long-range allosteric effect (think Gleevec's super strange mechanism of action). But, deepmind WILL be able to help design biologics that, for example, can interact with bump-hole mutations.

There was never an expectation that "protein folding" solves every problem in the drug discovery pipeline. That's out of scope for the basic problem.

As for this:

> AI methods rely on learning the rules of protein folding from existing protein structures.

Come on. There is no method that doesn't rely on learning the rules of protein folding from existing structures. Even de novo MD-modeling has tweaking fudge factors (we could call it "dark biochemical fields" -- think: what is the expected dielectric constant around a tyrosine residue?? No way we're calculating that from the schroedinger equation) that are empirically derived to get your results.