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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

#361

Post by layer8 »

WhompingWindows wrote: post #181
mabbo wrote: post #30

Sometimes announcements like this are a bit over-the-top. But what really, to me, cements the 'big-deal' of this is the "Median Free-Modelling Accuracy" graph half way down the page. Scores of 30-45 for 15 years. Now scores of 87-92. This isn't a minor improvement, it's a leap forward.

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 its predictions. Therefore it’s conceivable that AlphaFold is biased by the existing training data and doesn’t fully generalize to all proteins we would want to apply it to. Maybe that won’t be a problem in practice, but nevertheless it makes for a significant difference from what AlphaZero was about, being solely self-trained.

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

#362

Post by asah »

dekhn wrote: post #343
mjn wrote: post #341

Earlier quoted context omitted.

That's a good point; this system certainly didn't come from nowhere! The protein datasets they used also mostly came out of various NIH-funded projects. What I meant to focus on was that I think DeepMind has less of a pure money/scale advantage in this area than in some others. In something like Go or Atari game-playing, there are many academic groups researching similar things, but their resources are laughably smal…

Personally I think a major part of the secret sauce is Google's internal compute infrastructure. When I was an academic, 50% of my time went to building infra to do my science. At Google, petabytes of storage, millions of cores, algorithms, and brains were all easily tappable within a common software repo and cluster infrastructure. That immediately translates to higher scientific productivity.

Having recently experienced both, 1000x this.

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

#363

Post by jcims »

klmr wrote: post #337
whimsicalism wrote: post #299

Earlier quoted context omitted.

What do you mean? Genetics is amenable because the genome is a sequence that can be language modeled/auto-regressed for depth of understanding by the network. There are plenty of inferences that you would want to do on genetic sequences that we can't model exactly and there is some past work on doing stuff like this, although biology is usually a few years behind. https://www.nature.com/articles/s41592-018-0138-4 e:…

I meant, which specifics are you thinking of? > Genetics is amenable because it is a sequence Not sure what you mean by that. Genetics is a field of research. The genome is a sequence. And yes, that sequence can be modelled for various purposes but without a specific purpose there’s no point in doing so (and furthermore doing so without specific purpose is trivial — e.g. via markov chains or even simpler stochastic p…

Not being in the field, I would term what I see in this story as a ‘bottom up’ approach to understanding genetics/molecular biology. More akin to applied sciences than medicine or health. This, for example, seems to be very important but it still leaves us with a jello jigsaw puzzle with 200 million pieces and probably far removed from immediate utility in health outcomes.

Then there’s the more clinically oriented approaches of looking at effects, trying to find associated genes/mutations whatever mechanisms exist in between to cause a desirable or undesirable outcome. I’d call that ‘top down’.

I’m sure the lines get blurred more every day, but is there a meaningful distinction into these and/or more categories that are working the problem from both ends? If so, are there associated terms of art for them?

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

#364

Post by bayeslaw »

as so many time recently the hn crowd proves to be completely clueless and uneducated when it comes to ai.. this is a miracle.. it is THE achievement we'll remember from the past decade when it comes to ai.. if you don't understand why I recommend learning and reading.the level of ignorance and often proud ignorance here is frightening to me.. ppl who downplay this are either stupid in biochemistry or ai or both .. please don't listen to them. this right here is the single biggest news of 2020..

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

#365

Post by hailwren »

xphos wrote: post #346

Like this is awesome and a huge advancement but one thing that worries me with an AI solution is that it doesn't really draw us any closer to the why. Why do proteins fold the way they do? We can predict the resulting structure which is extremely significant, we have no clue why. While we get the insight of being able to predict some structures we don't get the insight of why things are happening the way they are. In…

There are two threads here. The first is that it would not be surprising to learn that describing the way that proteins fold is a very hard thing for humans to understand. See i.e. 4CT [1] and its computational proofs.

The second is that explainability in ML is much more tractable than it was 10 years ago. This is not to say that it's solved, but having solved the predictive problem -- I would expect model simplifications and SME research to proceed more quickly towards understanding the how now. I did some work w/ an Astrophysics postdoc using beta-VAEs [2] to classify astronomical observations, and simplifying models in order to achieve human-explainability proved to not cost as much predictive power as you might expect. It might be that the same holds true here.

1- https://mathworld.wolfram.com/Four-ColorTheorem.html

2 - https://paperswithcode.com/method/beta-vae

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

#366

Post by smt1 »

matsemann wrote: post #275
xral wrote: post #233

Earlier quoted context omitted.

AlQuraishi's tweet [0] about this: > CASP14 #s just came out and they’re astounding—DeepMind looks to have solved protein structure prediction. Median GDT_TS went from 68.5 (CASP13) to 92.4!!!! Cf. their 2nd best CASP13 struct scored 92.8 (out of 100). Median RMSD is 2.1Å. I think it's over https://predictioncenter.org/casp14/zscores_final.cgi [0]: https://twitter.com/MoAlQuraishi/status/1333383634649313280

What does that Å mean? Never seen our letter been used in a scientific context.

It used a lot when systems are examined at the nano-scale. Metrification and creating a "fubini's theorem" for a specific problem to measure something (indeed category theory is useful for building a localized "global wire" with appropriate "gauges" of interesting where optimization methods will work (to achieve the non-equilibrium control-theoretic orient-folds of interesting of whatever "the soln" is) with enough "space" to "try" pull-backs and push-forwards as needed (for a class/family of physically analogous of data). I thinking looking at things trough Joseph Fourier's eyes is pretty englightening. He sees to have ideated both the heat transfer problem (and being able to apply modern methodology by forming distributed or sparse representations of it, then assessing the non-linear dynamics of it modern robotics and mathematics senses to it, which would be very much applying pfaffian dynamics to me, and being able to know about cohomogies is a blessing such that the appropriate physical effect where the maximum likelihood is constained). is important in both scale free systems, fibers of networks of systems that need to be localized (this is approximately global sections of global optimization but then model indentified), mass effect which require some sort of techno-economic analysis (think the climate resilience problem) and (historically, I think COVID will shift that) lack of progress towards applied coding in the life sciences vs information sciences. What's pretty surreal to me is that exploring (and documenting some of interesting blurs between fields), say like math, physics, statistics, computer sciences, signal processing, natural language (even of language of scientific discourse), renormalization methods, naturalizations, socializations, and what are global/local laws lets you almost do a approach it as a "reverse Robin Hood" problem.

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

#367

Post by andi999 »

CJefferson wrote: post #23

Has anyone got any good other references for this? After some of the dodgy experiments related to alpha zero (comparing to purposefully degraded chess systems), I'd love to see some independent analysis.

I am also wondering. I generally find these kind of approaches hard to believe, but this might be my prejudices.

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

#368

Post by [deleted] »

curiousllama wrote: post #311
schemescape wrote: post #300

Did AlphaFold2 also have the biggest budget? :) Edit: from the other HN article on this topic: > We trained this system on publicly available data consisting of ~170,000 protein structures from the protein data bank together with large databases containing protein sequences of unknown structure. It uses approximately 128 TPUv3 cores (roughly equivalent to ~100-200 GPUs) run over a few weeks https://deepmind.com/blog/…

Actually, no! Or at least, the budget they used ( In other words, it's less like GPT3 and more like ImageNet.

[deleted]

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

#369

Post by bawolff »

Supermancho wrote: post #313
elcritch wrote: post #270

Earlier quoted context omitted.

It also makes one reconsider the notion that monopolies are entirely bad. This essentially appears to be a vanity project for Google. Though of course they'll benefit from it in many ways, but it's not like they're doing this as the core product of their service. It's a pretty awesome achievement.

> It also makes one reconsider the notion that monopolies are entirely bad. Much like political dictators, they can be exceedingly efficient and have resources (and authority) to do things in spite of opposing interests. People who faced with the narrative that countries have a monopoly on a number of aspects of life find monopolies are not a BAD THING(tm), but that they are bad for a consumer market - as a monopoly…

Or to put another way, the kings and queens of yesteryear funded a staggering amount of beautiful art, etc.

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

#370

Post by asah »

tuatoru wrote: post #280
baybal2 wrote: post #248

Earlier quoted context omitted.

One young lady I knew worked on neural algos recognition of X-ray images. They always had single digit, bizarre artifacts, where the program can't sometimes recognise the very data it was trained on with most minute differences. Other artifact was that the most "stereotypical cases" were least reliably recognised, and they hot a lot of flak for screwed up live demos, where a radiologist put a very, very obvious tumor…

I agree. The failures have to be explicable if we are to trust a model.

Doesn't it depends on the application ? i.e. some applications can tolerate false positives/negatives ?
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