12 ms·

What DeepMind’s AlphaFold 2 really achieved (2020)

224 points by apsec112 5y ago

jokoon 5y ago
Science is the only field where ML would truly shine and be really useful. There are tons of science problems where there are just not enough gray matter because it's just too expensive to train scientists. ML can crunch any data and result and speed up research by guiding experiments, where normal research just doesn't have enough resources to do so. Of course, it really only works if the scientists are able to understand data and how to use ML, which is why computing becomes just a tool for a scientist, nothing else. And again, ML is not really "smart", it's just sophisticated, improved statistical methods.
- siver_john 5y ago
  As someone who's background is biology and physics and who does ML work as well. This is an incredibly optimistic view of ML. >Of course, it really only works if the scientists are able to understand data and how to use ML, which is why computing becomes just a tool for a scientist, nothing else. Ideally in science you would like to use literally anything else other than ML if possible, fitting models come with their own challenges and neural networks are even more of a nightmare. Understanding the world well enough to hard code a rule is always preferable to fitting to some data and hoping the model will come up with a rule. While there has been some attempts to use ML for feature detection it then takes a lot of experimenting to generally show if it detected signal or just some noise in your data. Most of the things that would accelerate science would either require AI much more complex than we currently have (basically replacing lab assistants with AI) or are incredible research undertakings in their own right like Alpha Fold, Deep Potential Neural Networks, etc.
  - mjburgess 5y ago
    ML is an associative statistical system of function optimisation -- pretty much the opposite of science. Ie., ML makes the assumption that data points are IID. The whole purpose of science is to produce models which explain why data isnt IID.
    - blackbear_ 5y ago
      > ML is an associative statistical system of function optimisation You can also separate cause from effect by using causal inference, under some assumptions. > ML makes the assumption that data points are IID. Common ML algorithms do, but it is done for practical reasons rather than a limitation in the mathematics. > The whole purpose of science is to produce models which explain why data isnt IID. And ML can greatly help in this, though it is not a silver bullet.
      - jokoon 5y ago
        Actually, there might not be a good way to model or describe the difference between causal inference, correlation and causality. Causality involves a deep understanding of a phenomenon in science. For example, the standard model of physics is pretty good at describing the real world in a good enough manner because we understand a lot of it. The difference with correlation and causality, in my view, is human, scientific understanding of what things are. Formulas, data or drawing are not enough. For example there might never be a way to prove natural selection, even if there is a lot of data available, but a lot of scientific consensus is enough to describe causality.
    - jokoon 5y ago
      When you say it's the opposite of science, you mean ML is just made of black boxes that completely hide away knowledge that humans can interpret? Science is derived from scio, which in latin means knowledge. It's true that in a way, ML allows new things, but which are still obscuring real knowledge... I'm still curious about analysis of trained networks.
  - miltondts 5y ago
    Totally agree. While AlphaFold 2 is a tremendous achievement, to me the major drawback is the blackbox approach. It means it is very difficult to know when the model is outputting garbage and it also doesn't directly lead to new insights. A much more interesting approach: "Discovery of Physics From Data: Universal Laws and Discrepancies" [1] If ML did that, then it would be much more interesting. [1] - https://www.frontiersin.org/articles/10.3389/frai.2020.00025/full https://www.frontiersin.org/articles/10.3389/frai.2020.00025...
- temporalparts 5y ago
  > Science is the only field where ML would truly shine and be really useful. You know that ML is a really important part of a lot of companies that aren't "Science", right? Like Google search result rankings rely on ML.
- deeviant 5y ago
  > Science is the only field where ML would truly shine and be really useful. It's clear to see how ML is useful for science, but why exactly do you think it's *only* useful for science? It seems like in order for that to be true, you'd have to expand the definition of science to basically everything.
- omgwtfbbq 5y ago
  >Science is the only field where ML would truly shine and be really useful. There's a lot of hype about ML but this is a really bad take. Just take computer vision, you could come up with a dozen non science use cases in 5 minutes of brainstorming..
foxes 5y ago
So do you think it actually understands something about the structure of the protein folding problem? It somehow detected something about the physics, topology, the hard optimisation problem, and that it knows something about the geometry of that potential surface and can exploit that? Or is it just such a huge model it basically encodes an entire database after weeks and weeks of computation and has a more compressed form?
- wiz21c 5y ago
  My (basic) understanding is that 1/ there's some inductive bias (knowledge from researchers) 2/ data is definitely "compressed" in some ways 3/ since the model predicts better than the others, then it actually found some relationships in the data that were not found before. From what I understand, deep learning, although opaque and relying on tons of data, is a bit magical : although one would say "it's just probabilities", it actually does probabilities at a level where it actually figures some things. Plus, and that's very much a problem to me, Google does it at 100x the scale of a regular researcher. Since I just invested a year in studying data sciences, that worries me a lot : where am I suppose to work if, to produce meaningful results, you need way-too-expensive hardware...
  - thomasahle 5y ago
    > where am I suppose to work if, to produce meaningful results, you need way-too-expensive hardware... I know how you feel, but I also think stories like this may be a wake-up call for some groups to invest more in hardware. The Baker and Tsinghua University groups are not small. They can afford more than 4 GPUs. Probably it's more about setting up a good pipeline. Once you get about 4 GPUs you need more than one machine to run them. Hopefully in the next years we'll see more open source tools to make it easy to "build your own GPU cloud".
    - dumb1224 5y ago
      And to use all that GPU power exclusively you need some good strategy too. Speaking from a medium size biology research centre.
leadingthenet 5y ago
In case there’s someone else like me who could use an introductory video on the topic, Sabine Hossenfelder has recently made one: https://youtu.be/yhJWAdZl-Ck https://youtu.be/yhJWAdZl-Ck It includes some commentary on this discovery, as well.
- herodoturtle 5y ago
  That was a great intro, thanks.
- qwertox 5y ago
  https://en.wikipedia.org/wiki/Sabine_Hossenfelder https://en.wikipedia.org/wiki/Sabine_Hossenfelder
ma2rten 5y ago
(2020)
voiper1 5y ago
Needs (2020)
hortense 5y ago
One thing that DeepMind demonstrated was that sometimes one well funded team is much better than 50 poorly funded teams.
- deleted 5y ago
  [deleted]
- londons_explore 5y ago
  Most of deepminds additional funding goes into paying higher salaries. Those higher salaries don't result in better research results - but merely as a way to move the most prolific researchers from other institutions to them... Arguably this extra funding isn't leading to many new discoveries, but just shifting where discoveries are made.
  - nerdponx 5y ago
    > Arguably this extra funding isn't leading to many new discoveries, but just shifting where discoveries are made. Concentrating all these prolific researchers in one place, removing the publish-or-perish incentive, and giving them access to unlimited data and computing power. Seems like that could make a difference.
  - solveit 5y ago
    The additional funding raises the market price of researchers. That nudges the market to produce more researchers. The marginal quant became an AI researcher because people respond to incentives[1]. This leads to more new discoveries[2]. [1] Standard caveats apply and the point stands. [2] Standard caveats apply and the point stands.
    - alecst 5y ago
      I like how you added "and the point stands" to your own comment.
      - solveit 5y ago
wokwokwok 5y ago
This is from late 2020… weirdly, nothing seems to have come of it. Is there an update since then? Have they actually done anything useful with it?
- stupidcar 5y ago
  According to the AlphaFold Wikipedia article: > As of 18 June 2021, according to DeepMind's CEO Demis Hassabis a full methods paper to describe AlphaFold 2 had been written up and was undergoing peer review prior to publication, which would be accompanied by open source code and "broad free access to AlphaFold for the scientific community"
  - clavigne 5y ago
    which is basically admission that they will API it, not release the models... again. The original version on github can only compute the specific systems in the paper. https://github.com/deepmind/deepmind-research/tree/master/alphafold_casp13 https://github.com/deepmind/deepmind-research/tree/master/al... I don't know why scientific publications keep doing PR work for them.
    - AnotherTechie 5y ago
      if cryptography is a weapon, isn't folding proteins also arguably a weapon?
      - TenToedTony 5y ago
        Yes, but only because if cryptography is a weapon then everything is a weapon.
- drbw 5y ago
  As the article says > The details of how AlphaFold 2 works are still unknown, and we may not have full access to them until their paper is peer-reviewed (which may take more than a year, based on their CASP13 paper). So it's not particularly surprising that we haven't heard much yet.
optimalsolver 5y ago
What are the CASP competition equivalents of other scientific/engineering fields?
- danuker 5y ago
  Here are categories of benchmarks of Machine Learning papers that also publish code: https://paperswithcode.com/sota https://paperswithcode.com/sota
marsven_422 5y ago
Let's ML up some RNA and inject it! Sounds like a fantastic idea.
MauranKilom 5y ago
> Consider, for example, the possibility that Alphabet decides to commercially exploit AlphaFold, for example — is it reasonable that they make profit off such a large body of research paid almost exclusively by the taxpayers? To what extent is the information created by publicly available research — made public, mind you, to stimulate further public research — belong to the public, and under what conditions could it be used in for-profit initiatives? Maybe I have a wrong conception of what research is supposed to achieve, but commercializing new insights is absolutely one of the intended outcomes. One would sure hope that taxpayer money isn't funneled into research to... just enable more research. At some point the public should tangibly benefit from it, which is not achieved by writing more papers. This all notwithstanding the fact that DeepMind intends to make AlphaFold open source and available to the community.
- axiosgunnar 5y ago
  I suppose the grants should then be paid back over time with the money made? Perhaps with some interest, since the grants are high risk (many grants fail)
  - jahnu 5y ago
    I think that tax should cover that. Of course that raises the current problems with international firms and taxation.
    - axiosgunnar 5y ago
      Why do I have to pay the same tax rate as someone who literally got tax payer money injected into his budget, while I have to use surplus profit from the past?
      - elcomet 5y ago
        Because you profit from those inventions ? You might be saved by the drug discovered by Alphafold
      - 5y ago
londons_explore 5y ago
So I guess the next scientific milestone becomes doing the inverse of this challange... Ie. Given a structure that you'd like a protein to have, develop a sequence for it. If we could do that easily, we could start making molecular machines for all kinds of tasks. Rather than co-opting enzymes from nature, we could design our own. So many industries could benefit from that, even if you exclude all the biomedical applications where such an approach might be considered too high risk. We could for example begin with dishwashing tablets which actually get burnt on stuff off...
- hencoappel 5y ago
  I mean, the simplest solution to the reverse problem is generating random sequences and then predicting their structure to see if they fit the desired structure.
  - tryptophan 5y ago
    A 10 long protein has 21^10 possible sequences. That is already a hard number to guess-and-check. If you wanted to make a more reasonable length protein, of say 100-200, you would run out of atoms in the universe to do computations with.
- siver_john 5y ago
  So the Baker Lab out of Seattle has actually been working on that exact problem for a while now. There suit of programs for doing this type of work is called Rosetta and I know they have generated at least one protein from scratch.
  - titoCA321 5y ago
    They have and so has the Folding@Home team as Washington University. Although, Folding@Home is terribly inefficient at the way it approaches the problem. I know Rosetta but never have worked on it or used it so I can't comment on it's efficiency.
deeviant 5y ago
I'm I broken in some sort of way where I read: > Neither the Oxford Protein Informatics Group nor I accept any responsibility for the content of this post. And just stopped. I get it that you don't want to speak for your employer or academic institution but the "nor I" part is just weird.
- advisedwang 5y ago
  The author isn't saying they don't speak for themselves, they are just saying don't rely on this info. They say they wrote it to clear their thoughts, and probably haven't done a high level of verification.
- me_again 5y ago
  It's a law of the Internet that any post complaining about grammar or spelling must inevitably contain a grammatical error of its own. Yours is no exception. And without wishing to be unkind, the fact that you are adverse to this phrasing is not of general interest, which is likely why you're attracting downvotes.
  - deeviant 5y ago
    You seem to have failed to comprehend my comment. It has nothing to do with grammar, but rather the "I take no responsibility for the content of my post" (my summary of their wording), part.
  - tashi 5y ago
    Similarly, you probably meant "averse" instead of "adverse."
Synaesthesia 5y ago
This looks like a tremendous breakthrough in this domain, very impressive. I was similarly impressed by their Alphastar AI agent which could play Starcraft 2 at a pro level (this is actually a very difficult problem to solve). I'm similarly disappointed that, like with that effort, the methods and techniques will not be shared with the scientific community.
- inasio 5y ago
  This is definitely not a foregone conclusion, with AlphaFold 1 they did release a lot of information about it [0]. The article only says that google/deepmind is waiting until they publish the paper, and in fact Demis Hassabis recently tweeted that they plan to open source and provide broad access to it [1] [0] https://deepmind.com/research/open-source/alphafold_casp13 https://deepmind.com/research/open-source/alphafold_casp13 [1] https://twitter.com/demishassabis/status/1405922961710854144 https://twitter.com/demishassabis/status/1405922961710854144

224 points by apsec112 5y ago

Science is the only field where ML would truly shine and be really useful. There are tons of science problems where there are just not enough gray matter because it's just too expensive to train scientists. ML can crunch any data and result and speed up research by guiding experiments, where normal research just doesn't have enough resources to do so. Of course, it really only works if the scientists are able to understand data and how to use ML, which is why computing becomes just a tool for a scientist, nothing else. And again, ML is not really "smart", it's just sophisticated, improved statistical methods.

siver_john 5y ago
As someone who's background is biology and physics and who does ML work as well. This is an incredibly optimistic view of ML. >Of course, it really only works if the scientists are able to understand data and how to use ML, which is why computing becomes just a tool for a scientist, nothing else. Ideally in science you would like to use literally anything else other than ML if possible, fitting models come with their own challenges and neural networks are even more of a nightmare. Understanding the world well enough to hard code a rule is always preferable to fitting to some data and hoping the model will come up with a rule. While there has been some attempts to use ML for feature detection it then takes a lot of experimenting to generally show if it detected signal or just some noise in your data. Most of the things that would accelerate science would either require AI much more complex than we currently have (basically replacing lab assistants with AI) or are incredible research undertakings in their own right like Alpha Fold, Deep Potential Neural Networks, etc.
- mjburgess 5y ago
  ML is an associative statistical system of function optimisation -- pretty much the opposite of science. Ie., ML makes the assumption that data points are IID. The whole purpose of science is to produce models which explain why data isnt IID.
  - blackbear_ 5y ago
    > ML is an associative statistical system of function optimisation You can also separate cause from effect by using causal inference, under some assumptions. > ML makes the assumption that data points are IID. Common ML algorithms do, but it is done for practical reasons rather than a limitation in the mathematics. > The whole purpose of science is to produce models which explain why data isnt IID. And ML can greatly help in this, though it is not a silver bullet.
    - jokoon 5y ago
      Actually, there might not be a good way to model or describe the difference between causal inference, correlation and causality. Causality involves a deep understanding of a phenomenon in science. For example, the standard model of physics is pretty good at describing the real world in a good enough manner because we understand a lot of it. The difference with correlation and causality, in my view, is human, scientific understanding of what things are. Formulas, data or drawing are not enough. For example there might never be a way to prove natural selection, even if there is a lot of data available, but a lot of scientific consensus is enough to describe causality.
  - jokoon 5y ago
    When you say it's the opposite of science, you mean ML is just made of black boxes that completely hide away knowledge that humans can interpret? Science is derived from scio, which in latin means knowledge. It's true that in a way, ML allows new things, but which are still obscuring real knowledge... I'm still curious about analysis of trained networks.
- miltondts 5y ago
  Totally agree. While AlphaFold 2 is a tremendous achievement, to me the major drawback is the blackbox approach. It means it is very difficult to know when the model is outputting garbage and it also doesn't directly lead to new insights. A much more interesting approach: "Discovery of Physics From Data: Universal Laws and Discrepancies" [1] If ML did that, then it would be much more interesting. [1] - https://www.frontiersin.org/articles/10.3389/frai.2020.00025/full https://www.frontiersin.org/articles/10.3389/frai.2020.00025...
temporalparts 5y ago
> Science is the only field where ML would truly shine and be really useful. You know that ML is a really important part of a lot of companies that aren't "Science", right? Like Google search result rankings rely on ML.
deeviant 5y ago
> Science is the only field where ML would truly shine and be really useful. It's clear to see how ML is useful for science, but why exactly do you think it's *only* useful for science? It seems like in order for that to be true, you'd have to expand the definition of science to basically everything.
omgwtfbbq 5y ago
>Science is the only field where ML would truly shine and be really useful. There's a lot of hype about ML but this is a really bad take. Just take computer vision, you could come up with a dozen non science use cases in 5 minutes of brainstorming..

foxes 5y ago

So do you think it actually understands something about the structure of the protein folding problem? It somehow detected something about the physics, topology, the hard optimisation problem, and that it knows something about the geometry of that potential surface and can exploit that? Or is it just such a huge model it basically encodes an entire database after weeks and weeks of computation and has a more compressed form?

wiz21c 5y ago
My (basic) understanding is that 1/ there's some inductive bias (knowledge from researchers) 2/ data is definitely "compressed" in some ways 3/ since the model predicts better than the others, then it actually found some relationships in the data that were not found before. From what I understand, deep learning, although opaque and relying on tons of data, is a bit magical : although one would say "it's just probabilities", it actually does probabilities at a level where it actually figures some things. Plus, and that's very much a problem to me, Google does it at 100x the scale of a regular researcher. Since I just invested a year in studying data sciences, that worries me a lot : where am I suppose to work if, to produce meaningful results, you need way-too-expensive hardware...
- thomasahle 5y ago
  > where am I suppose to work if, to produce meaningful results, you need way-too-expensive hardware... I know how you feel, but I also think stories like this may be a wake-up call for some groups to invest more in hardware. The Baker and Tsinghua University groups are not small. They can afford more than 4 GPUs. Probably it's more about setting up a good pipeline. Once you get about 4 GPUs you need more than one machine to run them. Hopefully in the next years we'll see more open source tools to make it easy to "build your own GPU cloud".
  - dumb1224 5y ago
    And to use all that GPU power exclusively you need some good strategy too. Speaking from a medium size biology research centre.

leadingthenet 5y ago

In case there’s someone else like me who could use an introductory video on the topic, Sabine Hossenfelder has recently made one: https://youtu.be/yhJWAdZl-Ck https://youtu.be/yhJWAdZl-Ck It includes some commentary on this discovery, as well.

herodoturtle 5y ago
That was a great intro, thanks.
qwertox 5y ago
https://en.wikipedia.org/wiki/Sabine_Hossenfelder https://en.wikipedia.org/wiki/Sabine_Hossenfelder

ma2rten 5y ago

(2020)

voiper1 5y ago

Needs (2020)

hortense 5y ago

One thing that DeepMind demonstrated was that sometimes one well funded team is much better than 50 poorly funded teams.

deleted 5y ago
[deleted]
londons_explore 5y ago
Most of deepminds additional funding goes into paying higher salaries. Those higher salaries don't result in better research results - but merely as a way to move the most prolific researchers from other institutions to them... Arguably this extra funding isn't leading to many new discoveries, but just shifting where discoveries are made.
- nerdponx 5y ago
  > Arguably this extra funding isn't leading to many new discoveries, but just shifting where discoveries are made. Concentrating all these prolific researchers in one place, removing the publish-or-perish incentive, and giving them access to unlimited data and computing power. Seems like that could make a difference.
- solveit 5y ago
  The additional funding raises the market price of researchers. That nudges the market to produce more researchers. The marginal quant became an AI researcher because people respond to incentives[1]. This leads to more new discoveries[2]. [1] Standard caveats apply and the point stands. [2] Standard caveats apply and the point stands.
  - alecst 5y ago
    I like how you added "and the point stands" to your own comment.
    - solveit 5y ago

wokwokwok 5y ago

This is from late 2020… weirdly, nothing seems to have come of it. Is there an update since then? Have they actually done anything useful with it?

stupidcar 5y ago
According to the AlphaFold Wikipedia article: > As of 18 June 2021, according to DeepMind's CEO Demis Hassabis a full methods paper to describe AlphaFold 2 had been written up and was undergoing peer review prior to publication, which would be accompanied by open source code and "broad free access to AlphaFold for the scientific community"
- clavigne 5y ago
  which is basically admission that they will API it, not release the models... again. The original version on github can only compute the specific systems in the paper. https://github.com/deepmind/deepmind-research/tree/master/alphafold_casp13 https://github.com/deepmind/deepmind-research/tree/master/al... I don't know why scientific publications keep doing PR work for them.
  - AnotherTechie 5y ago
    if cryptography is a weapon, isn't folding proteins also arguably a weapon?
    - TenToedTony 5y ago
      Yes, but only because if cryptography is a weapon then everything is a weapon.
drbw 5y ago
As the article says > The details of how AlphaFold 2 works are still unknown, and we may not have full access to them until their paper is peer-reviewed (which may take more than a year, based on their CASP13 paper). So it's not particularly surprising that we haven't heard much yet.

optimalsolver 5y ago

What are the CASP competition equivalents of other scientific/engineering fields?

danuker 5y ago
Here are categories of benchmarks of Machine Learning papers that also publish code: https://paperswithcode.com/sota https://paperswithcode.com/sota

marsven_422 5y ago

Let's ML up some RNA and inject it! Sounds like a fantastic idea.

MauranKilom 5y ago

> Consider, for example, the possibility that Alphabet decides to commercially exploit AlphaFold, for example — is it reasonable that they make profit off such a large body of research paid almost exclusively by the taxpayers? To what extent is the information created by publicly available research — made public, mind you, to stimulate further public research — belong to the public, and under what conditions could it be used in for-profit initiatives? Maybe I have a wrong conception of what research is supposed to achieve, but commercializing new insights is absolutely one of the intended outcomes. One would sure hope that taxpayer money isn't funneled into research to... just enable more research. At some point the public should tangibly benefit from it, which is not achieved by writing more papers. This all notwithstanding the fact that DeepMind intends to make AlphaFold open source and available to the community.

axiosgunnar 5y ago
I suppose the grants should then be paid back over time with the money made? Perhaps with some interest, since the grants are high risk (many grants fail)
- jahnu 5y ago
  I think that tax should cover that. Of course that raises the current problems with international firms and taxation.
  - axiosgunnar 5y ago
    Why do I have to pay the same tax rate as someone who literally got tax payer money injected into his budget, while I have to use surplus profit from the past?
    - elcomet 5y ago
      Because you profit from those inventions ? You might be saved by the drug discovered by Alphafold
    - 5y ago

londons_explore 5y ago

So I guess the next scientific milestone becomes doing the inverse of this challange... Ie. Given a structure that you'd like a protein to have, develop a sequence for it. If we could do that easily, we could start making molecular machines for all kinds of tasks. Rather than co-opting enzymes from nature, we could design our own. So many industries could benefit from that, even if you exclude all the biomedical applications where such an approach might be considered too high risk. We could for example begin with dishwashing tablets which actually get burnt on stuff off...

hencoappel 5y ago
I mean, the simplest solution to the reverse problem is generating random sequences and then predicting their structure to see if they fit the desired structure.
- tryptophan 5y ago
  A 10 long protein has 21^10 possible sequences. That is already a hard number to guess-and-check. If you wanted to make a more reasonable length protein, of say 100-200, you would run out of atoms in the universe to do computations with.
siver_john 5y ago
So the Baker Lab out of Seattle has actually been working on that exact problem for a while now. There suit of programs for doing this type of work is called Rosetta and I know they have generated at least one protein from scratch.
- titoCA321 5y ago
  They have and so has the Folding@Home team as Washington University. Although, Folding@Home is terribly inefficient at the way it approaches the problem. I know Rosetta but never have worked on it or used it so I can't comment on it's efficiency.

deeviant 5y ago

I'm I broken in some sort of way where I read: > Neither the Oxford Protein Informatics Group nor I accept any responsibility for the content of this post. And just stopped. I get it that you don't want to speak for your employer or academic institution but the "nor I" part is just weird.

advisedwang 5y ago
The author isn't saying they don't speak for themselves, they are just saying don't rely on this info. They say they wrote it to clear their thoughts, and probably haven't done a high level of verification.
me_again 5y ago
It's a law of the Internet that any post complaining about grammar or spelling must inevitably contain a grammatical error of its own. Yours is no exception. And without wishing to be unkind, the fact that you are adverse to this phrasing is not of general interest, which is likely why you're attracting downvotes.
- deeviant 5y ago
  You seem to have failed to comprehend my comment. It has nothing to do with grammar, but rather the "I take no responsibility for the content of my post" (my summary of their wording), part.
- tashi 5y ago
  Similarly, you probably meant "averse" instead of "adverse."

Synaesthesia 5y ago

This looks like a tremendous breakthrough in this domain, very impressive. I was similarly impressed by their Alphastar AI agent which could play Starcraft 2 at a pro level (this is actually a very difficult problem to solve). I'm similarly disappointed that, like with that effort, the methods and techniques will not be shared with the scientific community.

inasio 5y ago
This is definitely not a foregone conclusion, with AlphaFold 1 they did release a lot of information about it [0]. The article only says that google/deepmind is waiting until they publish the paper, and in fact Demis Hassabis recently tweeted that they plan to open source and provide broad access to it [1] [0] https://deepmind.com/research/open-source/alphafold_casp13 https://deepmind.com/research/open-source/alphafold_casp13 [1] https://twitter.com/demishassabis/status/1405922961710854144 https://twitter.com/demishassabis/status/1405922961710854144