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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

#261

Post by dragontamer »

kxs wrote: post #205
sanxiyn wrote: post #188

Earlier quoted context omitted.

I expect this to be quickly replicated once published. Training data is public and training compute is not enormous and AlphaFold of 2018 did get replicated.

How do you define enormous? "It uses approximately 128 TPUv3 cores (roughly equivalent to ~100-200 GPUs) run over a few weeks". Also last time it took about a year for good replications to pop up.

A lot of labs have access to the various strategic supercomputers of the USA.

Ex: Summit has 27,648 V100 GPUs (and those V100s have Tensor units). If you're saying that only 200 GPUs are needed to replicate the experiment, that doesn't even use up 1% of Summit's available utilization.

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

#262

Post by btilly »

derefr wrote: post #37
fabian2k wrote: post #26

Earlier quoted context omitted.

Protein folding is a big and important problem, so this is certainly big news if it works as well as it seems. But I wouldn't assume that this changes everything, we can already determine how proteins fold by experimental work. The disadvantage is that this is a lot of work, though the methods there also improved a lot. One question is how robust the predictions are that DeepMind produces. I would also assume that ri…

We can already determine how a few proteins (170k — which sounds like a lot, but which is only 0.09% of all currently-catalogued protein sequences) fold by experimental work. What an accurate model of protein folding allows us to do, is to take our big database of DNA, predict protein foldings for all of it, and then stand up a search index for this database, keying each amino-acid "row" by the "words" of its predict…

We can already determine how a few proteins fold by experimental work.

Where "a few" is around 0.1% of the known 180 million proteins. So a relative few and a whole lot.

But the catch is which proteins could we figure out by experiment, and which not. In particular membrane proteins are hard to experimentally determine. But knowing how they fold is very important for figuring out how to get things to react with or get through membranes such as cell walls. Which is an important problem for everything from understanding how viruses work to targeted delivery of drugs. We now have a way to find those structures.

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

#263

Post by elcritch »

kxs wrote: post #205
sanxiyn wrote: post #188

Earlier quoted context omitted.

I expect this to be quickly replicated once published. Training data is public and training compute is not enormous and AlphaFold of 2018 did get replicated.

How do you define enormous? "It uses approximately 128 TPUv3 cores (roughly equivalent to ~100-200 GPUs) run over a few weeks". Also last time it took about a year for good replications to pop up.

A couple of hundred GPU's is well within the reach of many even moderately well heeled research institutes. It'd seem that about 3 weeks of compute time with 128 TPU v3's would be about $170,311.68.

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

#265

Post by mrDmrTmrJ »

beowulfey wrote: post #249
Seanambers wrote: post #179

Earlier quoted context omitted.

I particularly like the rant on pharmaceuticals companies lack of basic research. My impression has been that medical progression have been slow for quite some time, nice to see that there are some truth to that. In the end software and tech companies might just eat up the pharmaceutical industry as well. - It's all just code at some level. The Deepmind team did this with ; "We trained this system on publicly availab…

I mean, credit where credit is due. Google employs some of the greatest names in artificial intelligence and the DeepMind team had a huge chunk of them working on this problem. While the resources may have been available, I don’t think any other single institution had the level of brain power.

Absolutely. The capability to "create" the breakthrough is extremely rare. Perhaps only DeepMind, OpenAI, and GoogleBrain can assemble these types of teams. Luckily, the capability to replicate and exploit the breakthrough is far more 'common'; though still very rare.

Excited to see how follow on use of these models, by many more teams, researchers, and companies plays out over the next two decades.

This is a foundational advance!

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

#267

Post by ramraj07 »

the8472 wrote: post #7
partingshots wrote: post #3

I continue to be impressed by how quickly DeepMind has managed to progress in such a short time. CASP13 was a shocker to all of us I think, but many were skeptical as to the longevity of the performance DeepMind was able to achieve. I believe with CASP14 rankings now released, it's safe to say that they've proven themselves. Congratulations to the team! This work will have far reaching impacts, and I hope that you co…

> but many were skeptical as to the longevity of the performance DeepMind was able to achieve For a non-biologist, on what is this skepticism based? Just purely based on following ML news it looks like the trend for ML solutions has been that they've overtaken expert-systems once they've gained a solid foodhold in a field. Maybe this is some perception bias. Are there any cases where ML performed decently but then hi…

It's because for many researchers ML is just to take a standard keras or scikitlearn model shove their data in and get some table or number out, and see if that solves their problem. If that's your only ML experience then I suppose this is how sceptical you'd be of ML in general.

It looks like DeepMind invented a completely new method for this round that's not just an extension of their previous work, showing how much you can gain if you don't shoebox yourself into just trying to improve existing methods.

That all the scientists were highly skeptical about the scope of ML (and these are computer scientists to begin with mind you) just shows how little they knew of what they did know of what a computer or a program can possibly do, which is a bit appalling to be honest.

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

#268

Post by flobosg »

SubiculumCode wrote: post #264

RIP folding at home? EDIT: Just throwing this out there: Are there national security issues to think about with this? Can it be used to weaponize computational biology?

Folding@home tackles a related but different problem. They simulate folding dynamics, i.e. how does a protein reach its folded structure.

If AlphaFold gives you a picture of a protein structure, Folding@home shoots a video of that protein undergoing folding.

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

#269

Post by klmr »

whimsicalism wrote: post #5
partingshots wrote: post #3

I continue to be impressed by how quickly DeepMind has managed to progress in such a short time. CASP13 was a shocker to all of us I think, but many were skeptical as to the longevity of the performance DeepMind was able to achieve. I believe with CASP14 rankings now released, it's safe to say that they've proven themselves. Congratulations to the team! This work will have far reaching impacts, and I hope that you co…

Progress like this was, in my view, inevitable after the invention of unsupervised transformers. It'll be genetics next. e: although AlphaFold appears to be convolutionally based! I suspect that'll change soon.

> It'll be genetics next.

Which part of genetics are you thinking of? Much of genetics isn’t amenable to this kind of ML, because it isn’t some kind of optimisation problem. And many other parts don’t require ML because they can be modelled very closely using exact methods. ML does get used here, and sometimes to great effect (e.g. DeepVariant, which often outperforms other methods, but not by much — not because DeepVariant isn’t good, but rather because we have very efficient approximations to the exact solution).

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

#270

Post by elcritch »

beowulfey wrote: post #249
Seanambers wrote: post #179

Earlier quoted context omitted.

I particularly like the rant on pharmaceuticals companies lack of basic research. My impression has been that medical progression have been slow for quite some time, nice to see that there are some truth to that. In the end software and tech companies might just eat up the pharmaceutical industry as well. - It's all just code at some level. The Deepmind team did this with ; "We trained this system on publicly availab…

I mean, credit where credit is due. Google employs some of the greatest names in artificial intelligence and the DeepMind team had a huge chunk of them working on this problem. While the resources may have been available, I don’t think any other single institution had the level of brain power.

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.
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