No, DeepMind has not solved protein folding
Jeez.  Clickbait title, tons of fallacy, like goalpost moving.<p>Some context on myself:  I have 15+ years of postgraduate chemistry&#x2F;molecular bio&#x2F;biophysics&#x2F;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&#x27;m a pessimist about this AI innovation cycle&#x27;s prospects in general AI and in particular drug discovery.<p>Has protein folding been solved?  Yes.  As a practitioner, what I want to be able to do is pull a sequence that I&#x27;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had been solved, so I just did a dumb alignment and threading), identify that it was acting as an NPN transistor (unpublished details), fix the electron flow through the enzyme and improve the yield (<a href="https:&#x2F;&#x2F;link.springer.com&#x2F;article&#x2F;10.1186&#x2F;1754-1611-7-17" rel="nofollow">https:&#x2F;&#x2F;link.springer.com&#x2F;article&#x2F;10.1186&#x2F;1754-1611-7-17</a>).  Later, I looked at the structure of the enzyme, modified the surface charge in one particular part of it, and improved electron throughput again (<a href="https:&#x2F;&#x2F;www.mdpi.com&#x2F;1422-0067&#x2F;16&#x2F;1&#x2F;2020" rel="nofollow">https:&#x2F;&#x2F;www.mdpi.com&#x2F;1422-0067&#x2F;16&#x2F;1&#x2F;2020</a>).  This was with primitive &quot;protein folding through homology&quot; tools, now there&#x27;s an good chance I could do these sorts of things with proteins I don&#x27;t have homologous structure for.<p>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&#x27;s been buried.<p>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&#x27;s core fluorophore), or if you design a drug where you splice in an unnatural or D-amino acid, or oddities like that.<p>&gt; we are not at the point where this AI tool can be used for drug discovery<p>I disagree.  Sure, it probably won&#x27;t be able to find a small molecule binding site.  It almost certainly won&#x27;t be able to design a drug that has a long-range allosteric effect (think Gleevec&#x27;s super strange mechanism of action).  But, deepmind WILL be able to help design biologics that, for example, can interact with bump-hole mutations.<p>There was never an expectation that &quot;protein folding&quot; solves every problem in the drug discovery pipeline.  That&#x27;s out of scope for the basic problem.<p>As for this:<p>&gt; AI methods rely on learning the rules of protein folding from existing protein structures.<p><i>Come on</i>.  There is no method that doesn&#x27;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 &quot;dark biochemical fields&quot;  -- think: what is the expected dielectric constant around a tyrosine residue??  No way we&#x27;re calculating that from the schroedinger equation) that are empirically derived to get your results.