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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

#291

Post by visarga »

uoaei wrote: post #256

No, they didn't. They approximated a solution to protein folding. The two are different concepts -- this isn't the typical HN pedantry. "Solving" the problem would entail developing an interpretable algorithm for taking a string of amino acids and determining the 3D structure once folded. Approximating a solution would entail simulating that algorithm, which is what their neural network is doing. It is of course usua…

> "Solving" the problem would entail developing an interpretable algorithm

It looks like you'd like a grokable solution, but the problem might be just too complex to grasp for the human brain. "Solved" means they solved the protein puzzles on the official benchmark.

> but you would expect this with any suitable universal function approximator

Yeah, it's just that easy. Function approximator, engage! It took a team of Deep Mind researchers, two years and God knows how much compute. The universal function approximation theorem doesn't also say how to find that network.

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

#292

Post by elcritch »

kxs wrote: post #287
elcritch wrote: post #263

Earlier quoted context omitted.

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.

But of course that cost would only be for the final model. Anyway, I think I am just living in a different world... :-) We could never compete with that

Yah, big grant money. Now the grad students programming the open source clones will only make approximately $0.56, or 4.2 Ramen packs, for their effort. ;)

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

#293

Post by ramraj07 »

PaulDavisThe1st wrote: post #255
ramraj07 wrote: post #149

Earlier quoted context omitted.

It's probably never going to be solved though right. To truly solve protein folding we'd have to have a program that can stimulate a small but still significant system at the QM level; looks like deep learning can get us 60% (conservatively estimating the whole problem domain ) but not all the edge cases, just like it did in other problem domains as well.

Despite this breakthrough by DeepMind, at this point we still do not understand protein folding. That makes it very hard to say precisely which features would be required to do the simulation correctly. DeepMind/AlphaFold might have something to contribute there too, depending on how interpretable their network model(s?) are.

They seem to have a completely new tension algorithm that's doing the heavy lifting now, so it's likely we will learn much about how folding practically works from these results as well.

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

#294

Post by intpx »

elcritch wrote: post #263
kxs wrote: post #205

Earlier quoted context omitted.

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.

or cryptominers

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

#295

Post by deeviant »

uoaei wrote: post #256

No, they didn't. They approximated a solution to protein folding. The two are different concepts -- this isn't the typical HN pedantry. "Solving" the problem would entail developing an interpretable algorithm for taking a string of amino acids and determining the 3D structure once folded. Approximating a solution would entail simulating that algorithm, which is what their neural network is doing. It is of course usua…

> this isn't the typical HN pedantry.

Then launches into what can only be recognized as an exercise in pedantry.

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

#297

Post by ALittleLight »

ClumsyPilot wrote: post #108
entropicdrifter wrote: post #81

Earlier quoted context omitted.

170,000 is three orders of magnitude less than the number of recorded protein sequences. I don't think it's dismissive to describe that as comparatively few.

I don't know the field, and I understood 'a few' as like a dozen, certainly not in the thousands. Anyone uninitiated with think the same, and thise already informed. Well, they are already informed.

I also don't know the field and the opposite concern is that 170,000 sounds like a lot, but, apparently, it's a relatively small amount compared to the number of proteins there are. It makes sense to me to refer to it as a small number - e.g. "That hard drive is tiny." "No, it stores several million bytes..."

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

#298

Post by KKPMW »

uoaei wrote: post #256

No, they didn't. They approximated a solution to protein folding. The two are different concepts -- this isn't the typical HN pedantry. "Solving" the problem would entail developing an interpretable algorithm for taking a string of amino acids and determining the 3D structure once folded. Approximating a solution would entail simulating that algorithm, which is what their neural network is doing. It is of course usua…

This is exactly right. It's like saying you solved chess because for each configuration of pieces on the board you can use machine learning to predict whether that position can be achieved with valid chess moves. With 90% accuracy.

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

#299

Post by whimsicalism »

klmr wrote: post #269
whimsicalism wrote: post #5

Earlier quoted context omitted.

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 Deep…

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: for clarity

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

#300

Post by schemescape »

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/article/alphafold-a-solution-to-a-...

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