I've worked in bio and drug discovery for some 25 years. That includes building classifiers using gradient descent in the 90s (when algorithms, computers and data were all much worse). I ported DOCK to Linux in ~96 or 97. Since then I built an academic and then industrial career with some emphasis on using computing to solve problems in drug discovery, but I don't play that role any more.

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).

> ... but I don't play that role any more.

I was thinking of going into that field. Can you expand a bit on why you left?

Because programming computers is far more lucrative, and I'm better at it. However, if I had an unlimited budget I would return to biology.

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"

> much of my time is just telling scientists what I would do if I was in their place anyway.

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.

Most comp-biologists who work directly with programmers are some of the biggest jerks, and the least qualified tech folks.

They hide all of that under "I'm a scientist, you're not".

Maybe a culture clash? Academia is all about status and prestige - more often scientific outcomes seem to be a means to get the former (why journals don't publish negative results, why studies fail to replicate, why stuff isn't open access, why people worry about getting scooped, etc.)

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...

I haven't worked on the drug-side of things, but here my bio perspective: It's kind of out-of-vogue, but consider the "lock and key" model of proteins and small molecules (drugs). For drug design, what you want to do is get a key that fits just one lock (to pull whatever lever) and not others (to avoid side-effects). It's relatively easy to find a molecule that fits a protein, because that protein is what you might spend years researching and probing, but it's tricky to check if it does anything against ~100,000 others in humans. If you could do an in silico computational survey to be like, oh, maybe it'll target this accidentally, you could spot-check those in vitro, and/or stick on some other atoms to your small-molecule to make it not fit that off-target.

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).

First seems reasonable. I've not heard of anything on the later coming even close credibly - though is an obvious holy grail.

David Baker's group (author of the RoseTTAFold paper out today in science) has multiple exciting examples of de novo design of proteins.

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

> Could you give an overview of how people can leverage this (or how you might?).

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.

Also I would say that really they just made improvements to protein structure prediction, not protein folding which is the dynamic process by which proteins reach their equilibrium fold.

Most definitely.

I do agree with this but would add that you wouldn't want to do static docking to a single protein and sidechain configuration anyway so you're bound to find a usable forcefield. The incoming structure (ideally!) only needs to fall into the right energy landscape valley. If your forcefield and MD simulation can't keep this stable within the natural protein's configuration space you are probably not going to make progress, and you need the simulations to evaluate the natural energy landscape at body temperature, not cryo temperature.

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).

I think we largely agree.

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.

You can't do intelligent drug design if you don't know what the target protein looks like. We've gotten great at solving protein structures with things like crystallography and cryo-EM microscopy. Unfortunately, many interesting drug targets reside in the membrane of a cell, which means you can't easily work with them in a lab because they aren't soluble in anything but a plasma membrane. For instance, this is an issue with the 5HT2A protein, a g coupled protein receptor that is implicated in many serotonin related pathways.

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.

You should check out Salipro (https://www.salipro.com/) for membrane protein reconstitution.

Very interesting, thank you for the link.

It can be an aid in drug development, and can perhaps assist a bit in tuning small molecule drugs for more stable binding.

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.

For those that are unaware, industrial protein design is a multibillion dollar industry. For example, decades ago Genentech and Dow Corning formed a company that developed proteases (proteins that cut other proteins) that worked at much higher temperatures than the ones in nature. This was then sold to P&G and other major laundry companies (laundry detergent contains idle enzymes activated by the heat of the laundry water, and they go clean up. "Protein gets out protein" was the marketing jingle.

That was a few billion dollars right there and almost all the work was done by hand by lab scientists.

I work in cancer research with a drug discovery focus in a lab with some structure biologists. My understanding is that if we identified proteins targets suitable for therapeutics then understand its structure to identify secondary binding sites could be crucial for drug discovery. Drugs can then be designed to modulate its biological functions.