Could you give an overview of how people can leverage this (or how you might?).
From reading around about it, it sounds like there's often a need to find a certain type of molecule to activate/inhibit another based on shape and the ability to programmatically solve for this makes the searching way easier.
Is this too oversimplified/wrong? How will this be used in practice.
[Edit]: Thanks for the answers!
It doesn't look like the models produced by this would immediately turn the challenging problem of finding, approving, and marketing successful pharmaceuticals (IE, it doesn't eliminate any real bottleneck).
There was a long-term dream of structure-based drug discovery based on docking, but IMO, it has never really proved itself (most of the examples of success are cherry picked from a much larger pile of massive failures).
I was thinking of going into that field. Can you expand a bit on why you left?
I spent 15 years trying to be a professor and failed miserably. I was bad at it and didn't like what professors have to do.
I then moved to industry to be a random engineer and thrived doing things entirely unrelated to drug discovery. Eventually, I convinced my company to invest heavily in life sciences. This was successful and I was on track to be a powerful player (a "research engineer", just like the DM folks who are building these things) in this space, when the project got very popular and I was elbowed aside by others who are more aggressive. So I went back to being a programmer again, it's much less stressful, pays better, and realistically, much of my time is just telling scientists what I would do if I was in their place anyway.
"Don't swim with the sharks if you don't like being bitten"
That sounds familiar. I guess they mostly don't listen, whatever your record -- especially if it was in a different field they could learn from -- but I hope it's not always like that.
They hide all of that under "I'm a scientist, you're not".
Tech (at its best) hates credentialism (sometimes I think to a point of over-correction).
That said, 80% of the devs in the bay area seem to have gone to Stanford or MIT, so...
Holy grail, IMO, though is being able to design de novo protein sequences (to make "biologics", aka engineered protein drugs) that can a) target (bind/block/enhance) or do (chemical reactions) what you want and only that, b) are easily synthesizeable by bacteria/yeast (cheap to make), and c) are stable (easy to transport/store).
For example, see [1] or [2], and [2] was spun off into a company (Neoleukin Therapeutics).
[1] https://science.sciencemag.org/content/371/6531/eabc8182 [2] https://www.nature.com/articles/s41586-018-0830-7
Short answer: nobody knows. Traditionally, protein folding is a solution in search of a problem, but that's largely because the predictions were...unusably bad. This was always more of a super-difficult validation problem for the force fields and simulation methods, which could then be used for other problems of greater value (such as rational protein design, or simulation of the motion of proteins with known structures).
These predictions are better, but still pretty far from the level of precision that you'd want for any kind of rational drug design, where the exact locations of protein side-chains (for example) matter a lot. You'll note that AlphaFold returns structures that are "relaxed" using one of the oldest simulation systems for proteins: AMBER. So it's not exactly a clean-room solution to the problem, and you can't assume that the details (which matter to drug design) are going to be any better than for the older methods.
But that said, if you have a method that can reliably give you a blurry view of the overall shape of a protein, even that could be useful for things like target discovery or inference of biological networks. But this is still a lot closer to pure research than "revolutionizing drug discovery", as is frequently batted around on reddit, HN and the press.
There are some examples of this issue in the AlphaFold blog, some protein loops that they thought were mispredicted but it turned out they were part of an energy degeneracy so the natural state fluctuated pretty wildly, so if you can't simulate this properly it matters less how accurate the incoming structure is (to a certain degree of course).
Any drug target of any real-world interest is going to have local motion (i.e. floppiness that is dependent on circumstance and time) that matters at least as much to the finding of a drug than the fold of the protein itself.
One place I hesitate here is that step 0 to doing that kind of a simulation is having a decent starting structure. So maybe protein folding can help there. But I'm also skeptical, because the quality of the structure matters a great deal to getting a good simulation. A bad/blurry structure is of marginal use.
Being able to predict what it would look like would be a huge deal because then you can go about intelligently designing drugs for it.
Though I think the major impacts will be two-fold:
(1) The field of structural biology is going to see a change, with much more data available. Some structures of difficult to crystallize proteins will be solved, which may lead to much greater biological understanding. We may enter a time, where once you have a primary sequence, you also have a likely 3d-structure, which will probably change the daily work of quite a few biologists a bit.
(2) Industrial protein design. A tool such as this can potentially have great utility in optimizing proteins as chemical catalysts for various processes in different industries. This includes expanding the conditions under which a protein is active and also making their conformation more stable and so the protein more long-lived in solution.
That was a few billion dollars right there and almost all the work was done by hand by lab scientists.