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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

#201

Post by 0-_-0 »

mwcampbell wrote: post #173
cs702 wrote: post #118

Two years ago, after DeepMind submitted its first set of predictions to CASP (Critical Assessment of protein Structure Prediction), Mohammed AlQuraishi, an expert in the field, asked, "What just happened?" https://moalquraishi.wordpress.com/2018/12/09/alphafold-casp... Now that the problem of static protein structure prediction has been solved (prediction errors are below the threshold that is considered acceptable i…

How far does the similarity extend? Specifically, the big question for me is whether AlphaFold will be freely available like ImageNet, or proprietary.

ImageNet is a competition and a dataset, AlphaFold is a neural network.

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

#202

Post by pretendscholar »

vadansky wrote: post #128

Just to add to this whole "It's not solved! Yes it is!" discussion. Note that >According to Professor Moult, a score of around 90 GDT is informally considered to be competitive with results obtained from experimental methods. So if we go by >= 90 as solved: >In the results from the 14th CASP assessment, released today, our latest AlphaFold system achieves a median score of 92.4 GDT overall across all targets. they so…

87 GDT sounds pretty much solved to me if 90 is the benchmark

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

#203

Post by falcor84 »

6gvONxR4sf7o wrote: post #164

I hate headlines like “X has solved Y.” How often have we see computer vision and natural language solved at this point, whenever a model does well enough in a benchmark? Their own article doesn’t even have that headline. This is a massively cool thing that’s happened. Why ruin it with a massively hyperbolic headline?

I don't think I ever saw a headline saying natural language is solved; who's claiming that?

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

#204

Post by throwawayiionqz »

Seanambers wrote: post #179
flobosg wrote: post #171

Earlier quoted context omitted.

AlQuraishi described the progress made in CASP13 (2018) as “two CASPs in one”. This one is an even bigger breakthrough.

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…

This is the cost of training the final architecture with all the refinements enabled by years of research.

These years of research involved trying many different architectures, many of which received as much or more compute time than the final system.

The price of training the final architecture is meaningless. Researching and training AlphaGo was expensive but it enabled the ideas and development of AlphaZero which is more computationally tractable.

To have any chance, an academic team would need the same compute resources as what the DeepMind protein folding team used during the whole development of the architecture during the last few years, not only the resources used to train the final system. And I bet this funding is not available to most if not all academic teams.

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

#205

Post by kxs »

sanxiyn wrote: post #188
mwcampbell wrote: post #173

Earlier quoted context omitted.

How far does the similarity extend? Specifically, the big question for me is whether AlphaFold will be freely available like ImageNet, or proprietary.

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.

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

#206

Post by 0-_-0 »

asdfasgasdgasdg wrote: post #183
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.

I have a related question about this. If experimental methods produce results around a score of 90, what is the baseline we are comparing the DeepMind results against? If the experimental error is equal to the observed DeepMind error, how can we say which one is actually more erroneous?

That's a damn good question, it looks like we don't know how much above 90 AlphaFold is.

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

#207

Post by ramraj07 »

dekhn wrote: post #69
harperlee wrote: post #12

Not knowing a lot about biotechnology, I read the article and it sounds great, but how big is this as a gamechanger? Can someone comment on how big are the implications of this in, let’s say, 5 years from now, on day to day life? Does this mean that biotech is going to explode? Or just that drugs will come to market faster, perhaps cheaper for rare diseases, but from the same industry structure as always?

It seems unlikely there will be any large changes in life from solving protein folding. Knowing the structure of a protein (or really, its dynamics) is useful for identifying drugs that bind, but the real bottlenecks n drug discovery and biotech are elsewhere.

If folding and docking, alongwith dynamics simulations, start getting commodified, that might change things significantly though. I can already start imagining project workflows that are significantly streamlined without much thought, god knows what other scientists would dream up when we reach those steps

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

#208

Post by Rochus »

weregiraffe wrote: post #196
Rochus wrote: post #144

Earlier quoted context omitted.

The set of all proteins which can potentially be expressed in an organism is known. Now maybe we also get decent (static) structure information for these. But the interaction of a virus with the host cell is much more complex. There is much more than just an amino acid sequence involved. And these parts are all moving, so a static picture as we now can create faster than before does not contain all the information ne…

>The set of all proteins which can potentially be expressed is known. Sure, "known", but it's on the order of 20^10000. It won't fit in the entire visible volume of the universe.

No, the genome of the host is much smaller than the theoretical number of combinations. There are about 20 to 30k different proteins in a human cell (about 20k directly encoded on the DNA).

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

#210

Post by jeffbee »

allenz wrote: post #159
jeffbee wrote: post #53

Pretty interesting that they only used about $15k worth of resources (retail price) to achieve this. It's not a technique that would have been out of reach for other organizations based only on not being able to afford the compute.

That’s only for the final model. To find it, they’d need to run 1,000 experiments, trying many high-level approaches, many architectures for each component, hyperparameter search, and multiple seeds. Large machine learning projects need $10M in capital.

I bet it's still a lot less than they spent training AlphaStar.
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