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AlphaFold: a solution to a 50-year-old grand challenge in biology

deepmind.com

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Re: AlphaFold: a solution to a 50-year-old grand challenge in biology

#581

Post by dnautics »

rsfern wrote: post #565
selestify wrote: post #525

Earlier quoted context omitted.

If even the experimental approach is only 90% accurate, how do they know which 90% is accurate?

I’m not a protein crystallographer, but here’s my generalist take. We understand the physics of e.g. X-ray diffraction pretty well, so we can fit pretty decent forward models for the x-ray data given a proposed structure. The hardest task here is getting a good enough guess at the structure to optimize the physical model, and it’s my impression that people use an iterative model refinement workflow. At least that’s h…

X-ray diffraction is pretty nutty, too. You're taking the diffraction pattern, which is the fourier transform of the electron density. Fourier transform results are complex-valued data. Unfortunately, we don't really have X-Ray lasers, so you can only get the intensities and not the phases of those diffraction spots. Since mother science hates us, it of course the case that, in a fourier transform "more information" is contained in the phases than in the intensities.

So you "make guesses at what the phases are", the best choice is to bootstrapping these phases measured with another technique (you can introduce crystal defects that do allow you to guess at what the phases are).

Less scrupulous is to use a computer generated model, like fitting another protein "that you guess is related", then you model the electron density, take the phases of that.

In any case you take these "phase" guesses, and then apply it to your intensities, re-run the fourier transform, refine your electron densities, twiddle the location where you think the atoms, are, then repeat with your new model. This process repeats until you converge on a structure that you're happy with.

Now alarm bells should be screaming in your head right now: Yes, it's entirely possible to converge on a wrong structure, especially if you're a young up-and-comer professor seeking tenure that has no ethical problems with "suggesting" their grad students to sleep in the lab and work 100 hour weeks and willing to do slipshod work to get you tenure: https://www.sciencedirect.com/science/article/pii/S002228360...

Re: AlphaFold: a solution to a 50-year-old grand challenge in biology

#582

Post by root_axis »

mabbo wrote: post #416
treis wrote: post #143

Earlier quoted context omitted.

That is an impressive improvement, but I think you've missed the most important point: >a score of around 90 GDT is informally considered to be competitive with results obtained from experimental methods So DeepMind is to the point where it's a question of whether their generated model or the experimentally determined structure is closest to the actual physical structure.

Then we get the really fun question: if the experimentally determined structure is only 90% accurate, can machine learning actually reach 100%? Can you learn exact truth from inexact examples? Which gets into the concept of whether the ML model has actually learned some deeper conceptual ideas than we have, some deeper truth about how this works. If so, can we somehow extract that truth, or is it truly a black box th…

> Which gets into the concept of whether the ML model has actually learned some deeper conceptual ideas than we have, some deeper truth about how this works.

Well I think that the results speak for themselves; ultimately the question you raise is one of semantics. ML models don't think in terms of "conceptual ideas" like humans do, these models simply perform at such a massive statistical scale that they can identify patterns far beyond any human conception. Clearly, the model embodies some verifiably reliable information about the way the world works, but this is "just" a trick of statistics not anything resembling actual "understanding" in the way the word is typically used when referring to human understanding.

Re: AlphaFold: a solution to a 50-year-old grand challenge in biology

#583

Post by cma »

layer8 wrote: post #361
WhompingWindows wrote: post #181

Earlier quoted context omitted.

This reminds me of AlphaGo and AlphaZero. DeepMind was able to produce a very solid model on their first attempt, at both protein folding and at Go (and Starcraft2 as well). Their second models, however, seemed to blow their first out of the water. This bodes extremely well for the future of computational biology, I'm very excited thinking about the prospects. If we know how a protein folds, we know its shape, meanin…

One difference to AlphaZero though, if my understanding is correct, is that AlphaFold is trained on a predetermined data set and hence didn’t learn how “arbitrary” proteins fold in general, but just how the kinds of proteins fold for which we already know how they fold. To work more like AlphaZero, AlphaFold would have to be able to synthesize arbitrary proteins and run the experiments on them to verify and correct i…

> AlphaFold would have to be able to synthesize arbitrary proteins and run the experiments on them to verify and correct its predictions.

It can verify how much it minimizes the potential energy, which may not always line up with how it would fold in the real world but is a strong indicator.

Re: AlphaFold: a solution to a 50-year-old grand challenge in biology

#584

Post by calf »

Jach wrote: post #579
mnky9800n wrote: post #530

Earlier quoted context omitted.

People seem to forget that you need a system like academia that's allowed to fail. Most companies aren't allowed to fail when they need to have quarterly returns. Of course academia has become more and more competitive. But tbh I think the answer is that the funding hasn't increased equally with the number of quality people who could stay in academia. But who knows.

Stability "like academia" is rich, given all we've heard about "publish or perish". Modern academia is a poor fit for increasingly any case you can think of besides maintaining the status of academia. But sure, there needs to be some stability and ability to "fail"/i.e. produce something worthless. Corporate research departments provide this -- if they didn't, they wouldn't have a research department and indeed many…

That's all nice in theory, but what's the compelling empirical evidence for corporate science vs academic research? A professor might conversely argue the open nature of scholarship and freedom of inquiry as being essential to basic science, and capitalist businesses fundamentally cannot provide that. So it goes back to empirical support. And last I checked, companies still need a pool of trained PhDs to choose from, and those come from academia, for good reason.

Re: AlphaFold: a solution to a 50-year-old grand challenge in biology

#585

Post by bollu »

willj wrote: post #561
carlmr wrote: post #471

Earlier quoted context omitted.

If you have an experimental error that is somewhat normally distributed around the mean, the the AI should, with enough examples, learn what the rules are that are closest to the mean. Because it will minimize the sum of errors. So i do think the results could be more accurate than measurement.

I don’t think we can assume the errors are normally distributed. It’s possible researchers are biased in a particular “direction”, away from 0 on all dimensions of this problem.

That's fine. It's still a normal distribution with a different mean. The Gaussian is characterized by having only the first two moments: mean and variance.

Re: AlphaFold: a solution to a 50-year-old grand challenge in biology

#586

Post by sudosysgen »

drdeca wrote: post #549
sudosysgen wrote: post #540

Earlier quoted context omitted.

I just had a discussion with a friend about this! It's indeed a very difficult question. We ended on the conclusion that God can't possibly exist outside of time and space in the Abrahamic tradition because he precedes the creation of the Universe, but I'm sure there's a twist we missed somewhere.

I'm confused, because that seems backwards to me? How can He exist within time and space if He created the universe (including time and space)?

The conclusion we came to is that such a being would have to have it's own, metaphysically superior, time and space, and our time would be a subtime of it as well as our space would be a subspace of it.

The concept of a being subject to causality presupposes something akin to time.

Re: AlphaFold: a solution to a 50-year-old grand challenge in biology

#587

Post by sudosysgen »

chubs wrote: post #572
sudosysgen wrote: post #540

Earlier quoted context omitted.

I just had a discussion with a friend about this! It's indeed a very difficult question. We ended on the conclusion that God can't possibly exist outside of time and space in the Abrahamic tradition because he precedes the creation of the Universe, but I'm sure there's a twist we missed somewhere.

The answer i've read is that he _logically_ precedes it, not _temporally_. But yes that only makes a tad more sense :)

Hmm, I'd be interested to see how one could define logical causality without accidently defining something that has all of the properties of time!

Re: AlphaFold: a solution to a 50-year-old grand challenge in biology

#588

Post by prcrthr »

dalke wrote: post #403
_RPL5_ wrote: post #377

Earlier quoted context omitted.

The industry process will not change. You still need industrial biologists to generate and validate AphaFold structures, interpret the results as part of the bigger picture, and to finally design the drugs. And, then, of course you still need to validate the drugs in experimental systems (first the test tube, then mice, then humans). So your second guess is correct - one of the steps is much cheaper now, which margin…

> "armies of students who routinely spend 4-6 years of their PhD trying to solve a structure of a single protein" Back in the 1990s, when I worked on structure data, I remember that at least some crystallizations were easy enough they could be done as a rotation project. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6287266/ suggests that life is now a lot easier than the 1990s. Quoting the abstract: > Macromolecular…

I had a friend who solved the structure of 2 or 3 new proteins pretty much by himself his senior year of college. I also had an acquaintance who was a PhD student in the same lab, who said (jokingly) that she hated him because she had spent 5 years on a single protein and got way worse results than he did. I got the sense from talking to them that the process of figuring out how to get a protein to crystallize is basically just trial and error over and over—my friend himself said he basically got very lucky several times in a row (though he is also a brilliant biochemist).

Anyway that anecdote is pretty much the entire sum of my protein crystallography knowledge, but perhaps it explains how your experience and GP's statement can both be true?

Re: AlphaFold: a solution to a 50-year-old grand challenge in biology

#589

Post by Ericson2314 »

sudosysgen wrote: post #542
Ericson2314 wrote: post #510

Earlier quoted context omitted.

You've just describe why many Socialists 100 years were very skeptical of anti-trust as trying to sacrifice modernity to proper up a romanticized notion of the past as disaggregated pure-petit-bourgeois capitalism. Really not that different than the critism of the Luddites 100 years before that. See https://ilr.law.uiowa.edu/print/volume-100-issue-5/all-i-rea...

This line of argument reminds me of Haldane's point that economic planning can often work for the same reasons why large corporations and monopolies often work well too.

"The People’s Republic of Walmart"
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