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The blog is light on details but it appears Alpha is being used for branding here. AlphaFold seems to depart in a significant way from the perfect information g

The blog is light on details but it appears Alpha is being used for branding here. AlphaFold seems to depart in a significant way from the perfect information game playing agent's architecture. A few questions spring to mind: 1) What is the architecture of the generative network and where exactly does it fit in the pipeline? 2) What is the interaction with the database? Is there an encoder being trained with real sequences further augmented with variations using the generative network? 3) What is the structure of the neural network that encodes the sequence? Is it a graph network, LSTM or simple conv-net? 4) The gradient descent step is very vague. Is it a physically based differentiable model (not a neural network) whose parameters are being optimized with gradient descent using automatic differentiation? Or something else? In short, there's some detail on scoring but how are the proposals being generated? Questions aside, the results speak for themselves and are head and shoulders above all other showings. I wonder what it feels like for someone whose been in the field for years. Despite the high score, there's still a long way to go before results reach real world utility. It's also worth keeping in mind that from a systems biology perspective, protein folding is only a small part of what makes getting clinically useful results difficult. I might have missed something but I could not find anywhere indications of an intention to publish further details. That would be disappointing if such were indeed the case.

DrNuke 8y ago
I would question them more bluntly on the accountability issue though: the scientific community needs full reproducibility of exceptional results these days more than ever, so they should just make an effort on that front imho. They do not need to open source their code and their data in full, just planning a peer reviewed, open demonstration may suffice?
- 317070 8y ago
  The contest IS an open demonstration? You get unlabeled data, need to guess the labels and after you submit the labels the external comitee tells you how good you were. At this point it might be unreproducible, but the paper is about it being verified and demonstrated.
  - deleted 8y ago
    [deleted]
Jabbles 8y ago
Presumably this was timed to go with the start of the NIPS conference. Maybe they will present something there.
- plsgibfreelunch 8y ago
  Not necessarily. The CASP13 meeting where the results of the CASP competition are released is also taking place just now (Dec 1-4).
nopinsight 8y ago
It looks like some details might be published in the CASP13 conference. http://www.predictioncenter.org/casp13/index.cgi http://www.predictioncenter.org/casp13/index.cgi For such a breakthrough, there is a good chance they are writing another article for Nature or Science, even aiming for another cover feature.
hmartiniano 8y ago
I'm not really "in the field" but I did some computational work on protein folding in the past. I have a hunch on how they are doing this. From the blog post it appears that the network is using angles and distances of aminoacids in a given sequence in know structures to predict good starting point(s) for regular molecular dynamics-based structural optimization (what is called the "gradient descent step" in the post). If I'm wrong then I'll just have to try this approach myself one day...
- Cybiote 8y ago
  Yes, it seems you're very much on the right track according to page 12, direct structure optimization: http://predictioncenter.org/casp13/doc/CASP13_Abstracts.pdf http://predictioncenter.org/casp13/doc/CASP13_Abstracts.pdf The document also answers some of my questions. Some confusion from the fact that they are using multiple methods in parallel. The scoring networks are resnets. The database was used in training one of the networks. The generative network section is still unclear, but it looks like it was used in one of their methods, fragment assembly step and network architecture is DRAW (trained with DB). Network generated Fragments inserted using simulated annealing, ranked by either just their conv-net or both existing methods and conv-net. Simulated Annealing hyperparameters were optimized with evolutionary search. Their third approach is what is most similar to what you describe. It looks like they combine lots of approaches, including features, convnet scores and predictions from other methods. Still needs a lot of detail to be filled in such as more detail on how they integrate memory and how gradient descent is actually done directly on protein chain structures. It's possible their computational advantage made a large difference in being able to arrive at this result; existing researchers need not feel too bad.
- natechols 8y ago
  gradient descent != molecular dynamics; the latter is simulating forces on atoms, not attempting to optimize a target function. You can use molecular dynamics for optimization too - it can be very powerful, just slow - but I don't think that's what they're describing here.
  - electricslpnsld 8y ago
fjsolwmv 8y ago
The name of the company is Alphabet. Of course the prefix Alpha is a branding label.
- virgilp 8y ago
  That makes it sound like they're betting the company on "deep learning" ("Alpha").
  - jf- 8y ago
    Ooh. Interesting connection! You have to wonder if that wasn’t in the back of someone’s mind during the restructure.
    - jsty 8y ago
      According to Page at least [1], it was partially due to how 'alpha' is used in the trading world - to denote active returns, ie. returns above a benchmark [1] http://uk.businessinsider.com/why-googles-new-name-is-alphabet-2015-8 http://uk.businessinsider.com/why-googles-new-name-is-alphab...
ArtWomb 8y ago
Regarding gradient descent optimization over the global free energy of the entire protein structure, the CASP13 animated gif paints quite a picture. Have we ever seen anything with that degree of fidelity before? Ability to do this at scale could mean protein folding is effectively "solved" and we can move onto the next phases of computational design in systems biology. Namely: protein interaction prediction, design of antibodies, vaccines, novel assemblies, and on and on. There are >100K molecules in the Protein Data Bank. With new ones added everyday. But folding and interaction data is opaque. If immediate predictions could be made that will in turn create a feedback loop informing design. Actually pretty excited about the implications for Zero-G protein factories. Pharmaceutical companies could be a commercial driver for space based protein crystal fabs. The coming of age of de novo protein design https://www.nature.com/articles/nature19946 https://www.nature.com/articles/nature19946
- natechols 8y ago
  Many researchers have already moved on to the next phases, because those are ultimately far more useful problems than the pure guesswork of CASP contests. Of course better prediction tools will mean better designs too, but the existing tools were already powerful enough to yield some pretty impressive non-natural designs.

DrNuke 8y ago

I would question them more bluntly on the accountability issue though: the scientific community needs full reproducibility of exceptional results these days more than ever, so they should just make an effort on that front imho. They do not need to open source their code and their data in full, just planning a peer reviewed, open demonstration may suffice?

317070 8y ago
The contest IS an open demonstration? You get unlabeled data, need to guess the labels and after you submit the labels the external comitee tells you how good you were. At this point it might be unreproducible, but the paper is about it being verified and demonstrated.
- deleted 8y ago
  [deleted]

Jabbles 8y ago

Presumably this was timed to go with the start of the NIPS conference. Maybe they will present something there.

plsgibfreelunch 8y ago
Not necessarily. The CASP13 meeting where the results of the CASP competition are released is also taking place just now (Dec 1-4).

nopinsight 8y ago

It looks like some details might be published in the CASP13 conference. http://www.predictioncenter.org/casp13/index.cgi http://www.predictioncenter.org/casp13/index.cgi For such a breakthrough, there is a good chance they are writing another article for Nature or Science, even aiming for another cover feature.

hmartiniano 8y ago

I'm not really "in the field" but I did some computational work on protein folding in the past. I have a hunch on how they are doing this. From the blog post it appears that the network is using angles and distances of aminoacids in a given sequence in know structures to predict good starting point(s) for regular molecular dynamics-based structural optimization (what is called the "gradient descent step" in the post). If I'm wrong then I'll just have to try this approach myself one day...

Cybiote 8y ago
Yes, it seems you're very much on the right track according to page 12, direct structure optimization: http://predictioncenter.org/casp13/doc/CASP13_Abstracts.pdf http://predictioncenter.org/casp13/doc/CASP13_Abstracts.pdf The document also answers some of my questions. Some confusion from the fact that they are using multiple methods in parallel. The scoring networks are resnets. The database was used in training one of the networks. The generative network section is still unclear, but it looks like it was used in one of their methods, fragment assembly step and network architecture is DRAW (trained with DB). Network generated Fragments inserted using simulated annealing, ranked by either just their conv-net or both existing methods and conv-net. Simulated Annealing hyperparameters were optimized with evolutionary search. Their third approach is what is most similar to what you describe. It looks like they combine lots of approaches, including features, convnet scores and predictions from other methods. Still needs a lot of detail to be filled in such as more detail on how they integrate memory and how gradient descent is actually done directly on protein chain structures. It's possible their computational advantage made a large difference in being able to arrive at this result; existing researchers need not feel too bad.
natechols 8y ago
gradient descent != molecular dynamics; the latter is simulating forces on atoms, not attempting to optimize a target function. You can use molecular dynamics for optimization too - it can be very powerful, just slow - but I don't think that's what they're describing here.
- electricslpnsld 8y ago

fjsolwmv 8y ago

The name of the company is Alphabet. Of course the prefix Alpha is a branding label.

virgilp 8y ago
That makes it sound like they're betting the company on "deep learning" ("Alpha").
- jf- 8y ago
  Ooh. Interesting connection! You have to wonder if that wasn’t in the back of someone’s mind during the restructure.
  - jsty 8y ago
    According to Page at least [1], it was partially due to how 'alpha' is used in the trading world - to denote active returns, ie. returns above a benchmark [1] http://uk.businessinsider.com/why-googles-new-name-is-alphabet-2015-8 http://uk.businessinsider.com/why-googles-new-name-is-alphab...

ArtWomb 8y ago

Regarding gradient descent optimization over the global free energy of the entire protein structure, the CASP13 animated gif paints quite a picture. Have we ever seen anything with that degree of fidelity before? Ability to do this at scale could mean protein folding is effectively "solved" and we can move onto the next phases of computational design in systems biology. Namely: protein interaction prediction, design of antibodies, vaccines, novel assemblies, and on and on. There are >100K molecules in the Protein Data Bank. With new ones added everyday. But folding and interaction data is opaque. If immediate predictions could be made that will in turn create a feedback loop informing design. Actually pretty excited about the implications for Zero-G protein factories. Pharmaceutical companies could be a commercial driver for space based protein crystal fabs. The coming of age of de novo protein design https://www.nature.com/articles/nature19946 https://www.nature.com/articles/nature19946

natechols 8y ago
Many researchers have already moved on to the next phases, because those are ultimately far more useful problems than the pure guesswork of CASP contests. Of course better prediction tools will mean better designs too, but the existing tools were already powerful enough to yield some pretty impressive non-natural designs.