No, DeepMind has not solved protein folding
occamstypewriter.org
occamstypewriter.org
Some context on myself: I have 15+ years of postgraduate chemistry/molecular bio/biophysics/biochemistry experience, then quit to go into tech, worked for AI hardware and now AI-driven software startups, mostly as backend (not implementing ML models, but I know how to do that and have done some small models for myself). I'm a pessimist about this AI innovation cycle's prospects in general AI and in particular drug discovery.
Has protein folding been solved? Yes. As a practitioner, what I want to be able to do is pull a sequence that I've retrieved from DNA, drop it into a computer, and get the structure out. Intuitive insight can FOLLOW from those results. For example: In one of my projects, I was able to look at the structure (a homolog had been solved, so I just did a dumb alignment and threading), identify that it was acting as an NPN transistor (unpublished details), fix the electron flow through the enzyme and improve the yield (https://link.springer.com/article/10.1186/1754-1611-7-17). Later, I looked at the structure of the enzyme, modified the surface charge in one particular part of it, and improved electron throughput again (https://www.mdpi.com/1422-0067/16/1/2020). This was with primitive "protein folding through homology" tools, now there's an good chance I could do these sorts of things with proteins I don't have homologous structure for.
These are the sorts of things that protein folding enables. One more thought -- I bet that DeepMind can do things like make it obvious where there are certain posttranslation sites (like FeS clusters) or make it obvious where there is a cryptic phosphorylation or glycosylation site (sequence holdover from a previous mutant that no longer has its expected functionality) because it's been buried.
Will there be corner cases? Yes. Probably deepmind will have difficulty solving the fold of amyloid fibrils. Probably deepmind will have some difficulty with super-strange post-translational modifications (think things like GFP's core fluorophore), or if you design a drug where you splice in an unnatural or D-amino acid, or oddities like that.
> we are not at the point where this AI tool can be used for drug discovery
I disagree. Sure, it probably won't be able to find a small molecule binding site. It almost certainly won't be able to design a drug that has a long-range allosteric effect (think Gleevec's super strange mechanism of action). But, deepmind WILL be able to help design biologics that, for example, can interact with bump-hole mutations.
There was never an expectation that "protein folding" solves every problem in the drug discovery pipeline. That's out of scope for the basic problem.
As for this:
> AI methods rely on learning the rules of protein folding from existing protein structures.
Come on. There is no method that doesn't rely on learning the rules of protein folding from existing structures. Even de novo MD-modeling has tweaking fudge factors (we could call it "dark biochemical fields" -- think: what is the expected dielectric constant around a tyrosine residue?? No way we're calculating that from the schroedinger equation) that are empirically derived to get your results.
Not to give DeepMind too much credit, but it's also entirely possible that "both are correct". By the nature of how targets are selected for CASP, year over year there is higher likelihood that "pathologically difficult" proteins are presented for the competition, for example - a protein that exhibits polymorphic structure where the technique (say Xtal vs NMR) biases the "solved" fold in a huge way. I believe CASP tries to weed out proteins that we know are polymorphic, but you can never be sure, and again, as time marches on, those are the proteins that fundamentally are harder to solve, so it's likely they will be enriched in the test pool.
An extreme example is insulin. The structure of insulin is has been solved for 60? I think years, but when it's in the environment of its receptor, it looks TOTALLY different (solved 5? years ago). Having said that, doubtful that DeepMind could ascertain that structure, since the environment is super-super different.
I think that the modestly high error rate is an indication that deepmind is mostly interpolating and has solved the broad protein folding heuristic. It probably will get better.
Right but we also can't generalize from 2 out 3, and without knowing this figure it's really hard to say how useful this is, no?
> Knowing how ML works, I'm surprised it didn't give any indication of low confidence.
This is actually a hard problem in ML, for example in NLP, many people assume a high log prob score means a high confidence but it is not true at all.
Thank you for the clarification. I wasn't aware of this, as I'm most familiar with super-basic/old NLP techniques like BOW/RNN/LSTM/GRU techniques, where log prob scores seemed to me to be roughly correlated with result quality. I'm aware the landscape has changed recently with insights about high dimensional searches...
But after training you can recalibrate the temperature of the softmax on the test set and still get meaningful confidence scores (temperature calibration). Or you can use a variation of cross entropy called Focal Loss that will leave your logits un-squashed.
If it got answer right 1 time in 100, that would be amazing and you'd be foolish not to use it!
Except you have no way of knowing if the answer it gives you is one of the 66 right predictions vs one of the 33 wrong predictions. You could say it's likely correct, but not to a high enough degree of confidence that you could really trust it without verifying using the old established techniques.
Also important - verifying that a given model has a signature that matches the established techniques is far easier than using those techniques to generate the complete model from scratch.
I'm not really sure what your point is.
This isn't really how it works.
To quote the CASP competition organisers:
The organizers even worried DeepMind may have been cheating somehow. So Lupas set a special challenge: a membrane protein from a species of archaea, an ancient group of microbes. For 10 years, his research team tried every trick in the book to get an x-ray crystal structure of the protein. “We couldn’t solve it.”
But AlphaFold had no trouble. It returned a detailed image of a three-part protein with two long helical arms in the middle. The model enabled Lupas and his colleagues to make sense of their x-ray data; within half an hour, they had fit their experimental results to AlphaFold’s predicted structure. “It’s almost perfect,” Lupas says. “They could not possibly have cheated on this. I don’t know how they do it.”[1]
So you have experimental results, but still don't know how it folds. You aren't trying to avoid the all the experiments, just understand them.
[1] https://www.sciencemag.org/news/2020/11/game-has-changed-ai-...
When you set a threshold you improve precision at the detriment of recall. It's a tradeoff you can play with, but the score depends on it.
This seems to be the core of the article's arguments. Is this ratio high enough to claim the problem is "solved"?
If it was possible to accelerate drug discovery by looking at computed protein structures rather than protein structures established experimentally, it has now become plausible to do so. It has crossed a tipping point.
Since I'm not aware that there has been any drug discovery that has strongly depended on computational protein folding, this possible benefit remains to be seen.
[I worked in a lab which was heavily involved in CASP, the protein folding competition, but I was not myself involved]
Even though Google only won 4/5 games against Lee Sedol, that was the moment that sticks in peoples minds as when Google solved the problem of computer Go. What happened afterwards was a process of perfecting the algorithm. It seems likely the next few years will bring the same for AlphaFold.
https://twitter.com/TassosPerrakis/status/133353559400213299...
Tassos is also a professor in structural biology. Discovering structures the experimental way.
EDIT: on the other hand he also agrees that it's not the end of experimental structural biology
https://twitter.com/TassosPerrakis/status/133402467831107993...
>I have 15+ years of postgraduate chemistry/molecular bio/biophysics/biochemistry experience, then quit to go into tech, worked for AI hardware and now AI-driven software startups, mostly as backend
What made you quit biochemistry and enter tech and AI?
As someone who used to work in adjacent field, I have a question that I hope you can help me with. DL usually requires a large volume of data, but I don't know whether there is a huge pile of experimentally determined protein structures for training. Did DeepMind find a clever method to get around data-size issue or there is really a lot of known protein structures? I skimmed one of their earlier papers and it seems training data was in tens of thousands. I am surprised that is enough for training DL structure prediction.
Also I am not in ML research, but IIRC the spooky/counterintuitive nature of high-dimensional gradient-based search is that you can get better results from less data by increasing the number of parameters, as long as you have a sane set of regularization techniques (or have we even ditched those too?).
Maybe. In the context of natural language at least, Transformers require less and less data to reach the same result as you increase the number of parameters. No regularization needed. See Figure 2 in the paper Scaling Laws for Neural Language Models (2001.08361).
It's quite odd. Who knows if that will hold for other domains, like protein folding. It may very well be the case though, since AFAIK DeepMind's folding model used attention to reach these landmark results.
It's true you don't need regularization if you've never seen the same data twice, but that's a similar regularization to early stopping. You'd expect the larger number of parameters would make the training error drop faster with fewer tokens as well due to improved optimizability. But rather than "larger models need less data", I'd say the take away is more "larger models need fewer steps to optimize training error". None of the models get "good" until they've seen a number of tokens similar to the number of parameters.
Unless you've run many epochs on small data sets and seen the same results, in which case that's pretty weird/cool.
It is interesting that larger models with regularization (early stopping) seems to work better than than training smaller models to convergence though.
But they added a ton of highly domain specific features. And since it's essentially a natural phenomenon, I would expect the signal to be relatively strong.
I recommend the video, or any video from Yannic:
https://youtu.be/B9PL__gVxLII'm not AS concerned about something like Ebola, because while being horrible, it's horribleness is self limiting because people die (relatively quickly) before they can spread it too far.
On the other hand, weaponized vCJD could have a population die off en masse years after they're infected. Since it can be transmitted via blood transfusions, it'd essentially shut down blood donations (although if everyone had it, I guess that wouldn't matter). Also just the fact that prion diseases can cause psychological symptoms make the implications different from something like a classical infectious disease.
Maybe the infection could use genetic mechanisms so that the infected pass it on to their children. Maybe this already happened thousands of years ago. X-Files Theme.
My question are as follows:
1. Does AlphaFold ever converge on structures in this fashion at all? As in, is the heuristic able to identify partial folds, or, is foldability somehow encoded either through evolutionary history?
2. If it does, are the structures that it identifies truly compact (measurable through sedimentation centrifugation) and solvable using conventional techniques? I suspect if the first than definitely the second because of sample bias.
3. If it does identify compact structures, are they novel sequences or sequences homologous to existing structures? If the former, then AlphaFold is truly extrapolating. If the latter, then it's only interpolating.
It would be super cool if AlphaFold discovered a "new fold" (for those who are unfamiliar with the jargon, a fold is a family of homologous sequences that create defined structure, novelty is gauged by homology, not the end structure; you can have the same structure created by wildly different sequences). It would be even cooler if AlphaFold discovered a new motif (besides alpha helix, beta sheet, and the known turns/chains). But I think that is rather unlikely.
4. If you change your bag distribution by using a primitive aa set (delete histidines, tryptophans, enrich for leucines and serines) how do your search dynamics change?
Pretty much everything in the post is true, the title isn't clickbait, and I didn't see any fallacy. It's only "goalpost moving" if you confused the location of the goalposts in the first place. For the record: I have worked on protein folding, I did a PhD in a closely related area, and I work in computational structural biology professionally now.
The OP is saying that the general problem isn't solved, and provided (IMO factual, true) explanations why there's still a long way to go. Arguing that it's OK for some uses (which is what you're doing) is true, but not a rebuttal.
Maybe it's possible to use this method to come up with models "good enough" for rigid-body docking, or to get the overall idea of a fold. But it's also true that these structures aren't going to be good enough for any kind of drug discovery, which is what TFA says.
It is my judgement that if you presented these results in 2003 anyone would have said "the protein folding problem has been solved". Nobody then expected "the solution to protein folding" to solve every single protein, and it's silly to now. If you're complaining about the 33% error rate, you don't know enough about ML to realize that that gap is going to get closed, and rapidly.
My argument is that it is "good enough for the uses that are in the domain of protein folding". There are a lot of things that are adjacent to protein folding (enzyme pocket analysis .e.g), but they never have been considered to protein folding per se.
"It is my judgement that if you presented these results in 2003 anyone would have said "the protein folding problem has been solved"."
Your judgment is your own. At the very least, your use of the word "anyone" should give you pause.
"Nobody then expected "the solution to protein folding" to solve every single protein, and it's silly to now."
Not the argument I'm making.
"If you're complaining about the 33% error rate, you don't know enough about ML to realize that that gap is going to get closed, and rapidly."
Not the argument I'm making.
"My argument is that it is "good enough for the uses that are in the domain of protein folding"."
I have no idea what that means. Uses "in the domain of protein folding" are equivalent to uses "in the domain of structural biology". So obviously, it depends on the quality of the structure being generated.
"There are a lot of things that are adjacent to protein folding (enzyme pocket analysis .e.g), but they never have been considered to protein folding per se."
You seem to have some internal dialogue where there is a field called "protein folding", where there is an unambiguous threshold for success that has now been crossed. There is no such field, and there is no such threshold.
Are these structures good enough to do some kinds of things, like structural genomics or docking? I dunno...maybe? Are they good enough to do structure-based drug design? Not from what I've seen. TFA seems to be saying the same thing, and your comment was that TFA is just wildly wrong, which seemed...unfair, at best.
You can bin all astounding ml results into one of two categories, convolutional forms or sequential forms. The problem space does not fit into either of those so cleanly.
Yes, you can turn a molecule into a graph, and there are graph ml techniques, but how that interacts with a protein also does not have a lot of source data (real or synthetic) nor is the interaction space obviously reducible into a differentiable form. Either would be sufficient for today's breed of ML to excel at this.
No. Look a the actual competition results.
> As a practitioner, what I want to be able to do is pull a sequence that I've retrieved from DNA, drop it into a computer, and get the structure out. Intuitive insight can FOLLOW from those results.
According to the competition results, you cannot do that using AlphaFold2.
- Professor John Moult
Co-Founder and Chair of CASP, University of Maryland
Gee, the Co-Founder of the competition calls it a solution. I wonder if that might be because this is a solution to the problem as he A) Knows much more about the problem than you B) has actually personally seen the results and C) has far more context than you to base his decision to call it a solution.
So Lupas set a special challenge: a membrane protein from a species of archaea, an ancient group of microbes. For 10 years, his research team tried every trick in the book to get an x-ray crystal structure of the protein. “We couldn’t solve it.”
But AlphaFold had no trouble. It returned a detailed image of a three-part protein with two long helical arms in the middle. The model enabled Lupas and his colleagues to make sense of their x-ray data; within half an hour, they had fit their experimental results to AlphaFold’s predicted structure. “It’s almost perfect,” Lupas says. “They could not possibly have cheated on this. I don’t know how they do it.”
Sure, you want to validate thing experimentally, but that isn't different to imaging.
[1] https://www.sciencemag.org/news/2020/11/game-has-changed-ai-...
https://news.ycombinator.com/reply?id=25262458&goto=threads%...
So they bootstrapped the phases of the X-ray diffraction pattern with the phases from AlphaFold. This does mean, though, that we must be critical - it is possible that they are converging to nonsense that AlphaFold has generated. To be truly sure, you must have some sort of independent confirmation. Might still be "good enough" depending on what your application is.
It's a fairly long read, but he goes into a lot more detail as to why the problem isn't yet solved. Interestingly, he also notes that pharma and academia should feel some embarrassment from DeepMind's achievement:
"What is worse than academic groups getting scooped by DeepMind? The fact that the collective powers of Novartis, Pfizer, etc, with their hundreds of thousands (~million?) of employees, let an industrial lab that is a complete outsider to the field, with virtually no prior molecular sciences experience, come in and thoroughly beat them on a problem that is, quite frankly, of far greater importance to pharmaceuticals than it is to Alphabet. It is an indictment of the laughable “basic research” groups of these companies, which pay lip service to fundamental science but focus myopically on target-driven research that they managed to so badly embarrass themselves in this episode."
[1] https://moalquraishi.wordpress.com/2018/12/09/alphafold-casp...
(Edited to clarify that the blog post was not recent.)
Deepmind have at least a few thousand employees, I'm willing to go out on a limb and say that the industry doesn't have "hundreds of thousands" of people working on protein folding alone.
What's important is I think which people they hired, as well as Deepmind-internal infrastructures. You can just walk over to one of the world experts in deep learning and ask them how to address some issue.
In pharma companies they have people who are really smart about drugs and biology, but ML experts would all have to be hired externally, and likely don't represent the top of the field as well as Deepmind does.
I don't know how UK R&D tax works, but most countries allow companies to write off some profits against R&D. Apart from the fact Google wants to do research, having a large center based in the UK probably helps their UK (and formerly EU?) tax position.
> CASP14 #s just came out and they’re astounding—DeepMind looks to have solved protein structure prediction. Median GDT_TS went from 68.5 (CASP13) to 92.4!!!! Cf. their 2nd best CASP13 struct scored 92.8 (out of 100). Median RMSD is 2.1Å. I think it's over
Here is what Mohammed AlQuraishi said in 2020:
CASP14 #s just came out and they’re astounding—DeepMind looks to have solved protein structure prediction. Median GDT_TS went from 68.5 (CASP13) to 92.4!!!! Cf. their 2nd best CASP13 struct scored 92.8 (out of 100). Median RMSD is 2.1Å. I think it's over [0]
and
“I think it’s fair to say this will be very disruptive to the protein-structure-prediction field. I suspect many will leave the field as the core problem has arguably been solved,” he says. “It’s a breakthrough of the first order, certainly one of the most significant scientific results of my lifetime.” [1]
[0] https://twitter.com/MoAlQuraishi/status/1333383634649313280
For instance, the gentlemen who was presented as a skeptic is instead shown to also say it's solved, and in reply to this you say "everyone" is looking at the "wrong" metric, and it allows errors of 8+ atom widths - he notes the median error is 2.1 angstroms, or 2.1 atom widths.
It's a lack of vision and leadership from pharma labs.
That's probably true, but I don't think Pfizer could have beat DeepMind, even if they had really tried. DeepMind is in a unique position to recruit lots of young deep learning researchers.
[1] https://blogs.sciencemag.org/pipeline/archives/2019/09/25/wh...
They also have theory groups which try to do structure prediction and drug docking but I don't think those groups get any respect inside the companies any more.
The author acknowledges as much in the preamble, "my mood lifted during the meeting due to the general excitement and quality of discussions, [...] my tribal reflexes gave way to a cooler and more rational assessment".
John Moult: ''' “This is a big deal,” says John Moult, a computational biologist at the University of Maryland in College Park, who co-founded CASP in 1994 to improve computational methods for accurately predicting protein structures. “In some sense the problem is solved.” '''
Andrei Lupas: ''' AlphaFold is unlikely to shutter labs, such as Brohawn’s, that use experimental methods to solve protein structures. But it could mean that lower-quality and easier-to-collect experimental data would be all that’s needed to get a good structure. Some applications, such as the evolutionary analysis of proteins, are set to flourish because the tsunami of available genomic data might now be reliably translated into structures. “This is going to empower a new generation of molecular biologists to ask more advanced questions,” says Lupas. “It’s going to require more thinking and less pipetting.” '''
Mohammed AlQuraishi: ''' “I think it’s fair to say this will be very disruptive to the protein-structure-prediction field. I suspect many will leave the field as the core problem has arguably been solved,” he says. “It’s a breakthrough of the first order, certainly one of the most significant scientific results of my lifetime.” '''
* What degree of accuracy is attainable via each technique?
* How much (a) wall clock time / (b) overall compute resources are required for each technique?
* What use case(s) fit best with each technique?
I offer these because I see a lot of energy expended in people digging in and defending their definitions, rather than understanding what other people mean.
There aren't more significant research towards finding new ways of solving DNA sequencing since this method is good enough and can improve from more data and better models.
We consider it "Solved" in this case.
Tons of tooling was built on top of it and until we can get the true sequence of amino acids quickly and cheaply, it's not going away.
Having a high enough accuracy will give you a "good idea" of the interactions it might have with other proteins and substances, but can't account for the millions of other interactions they might have with other particles.
Most of the bioinformatics aren't deterministic, but still rely on stochastic measures. DNA sequencing is done by sampling, then predicting the rest and as far as my own biology teachings go, it's categorized as "solved". Sequencing might get better, but we've accepted it as a solution for the moment.
Arguing the term "solved" is just pedantic. We know it's not 100%, but the actual usefulness of improving the prediction of a few more Angstroms isn't going to make a huge practical difference.
What matter is that we can start actually building tooling and lab tests on this method.
The big challenge for these kind of models is the curse of dimensionality. Since every atom in the structure can potentially interact with every other atom, it's tricky to make a joint distribution for the entire sequence and it's rare to have a model that's both accurate and parallelizable, so the field hasn't benefitted much from the advances in for instance GPU computing
I'm not sure what you mean by "DNA sequencing is done by sampling then predicting the rest". DNA sequencing works by oversampling and then making a "call" about the specific base in a position given the evidence. Regions without data are described as N with estimated length M, rather than a "prediction".
Given the technical limitations of single cell techniques it seems you both could be technically correct regarding the sampling.
Usually chaperones just accelerate the process, but some proteins requires chaperones to fold properly. chaperones. some of them prevent or correct damage caused by manifolding.
You can conduct cheap-and-easy experiments to verify the results, as opposed to imaging which doesn't always work anyway.
Isn't it amazing that the same model (transformer) is now SOTA in both language and proteins? Seems like the real story here is the benefits we could get from the transformer in many different fields, not just NLP.
I would imagine there's also benefits to studying _unknown_ proteins, and even being able to work backwards from desired characteristics to discover possible new ones.
unfortunately there is no clear next step for the disease biology part of the study, as far as I can tell, except to collect enormous amounts of high quality data about diseases, typically one or a few at a time, and hope you get lucky finding something (IE, serendipity is just as important as intelligence).
https://explainthispaper.com/ai-solving-protein-folding/
Another supporting article from Derek Lowe's (think Medical Science's Stratchery. Highly acclaimed and usually cynical) blog : https://blogs.sciencemag.org/pipeline/archives/2020/11/30/pr...
1. "...only two-thirds of DeepMind’s solutions were comparable to the experimentally determined structure of the protein. ..."
2. "... the average or root-mean-squared difference (RMSD) in atomic positions between the prediction and the actual structure is 1.6 Å (0.16 nm). That’s about the size of a bond-length."
3. It may be "... more difficult to predict the structures of proteins with folds that are not well represented in the database of solved structures."
4. "... the method cannot yet reliably tackle predictions of proteins that are components of multi-protein complexes."
- I thought that open code was becoming the standard and also being pushed by Google? But apparently this does not apply to deepmind, because of $$$s? For the original Alphafold (which actually was 3 models) no code is available except, where it had to be (their nature publication on one of the three models used in the competition).
- why did they not participate with all the available proteins? I guess it's some loophole in the rules, to allow for greater "improvements" even when models are not super general, but from a naive scientific view that is absolutely stupid.
- maybe they went fully generative this time, but in CASP13, 2/3 of their models were just blackbox-predictors, which they optimized with simulated annealing. Given that the configurational sample space for the protein is huge, doing that seems still rather costly. I wonder how the actual spectra fitting works and compares to that and why experimentalists could not go this route as well? (just do simulated annealing, until the spectrum fits).
- they already trained on all known proteins, yet with some they are still far off. Seems like it's not solved really, though results are certainly impressive and it could be a great tool for any person interested in frozen protein structures.
> That advance will be much clearer once their peer-reviewed paper is published (we should not judge science by press releases), and once the tool is openly available to the academic community
DeepMind has a tendency for hyper inflated PR (not the only ones mind you), wanting for the scientific process to run its course before claiming victory sounds good to me.
Can you point at some?
AlphaZero (emphasis: AlphaZero, not AlphaGo) is probably the most significant AI breakthrough of the last 25 years (maybe more - I think it's more significant than AlexNet on ImageNet) and there was very little hype about it.
AlphaGo got quite a lot of PR, but a lot of that came from the Go community, especially in Korea.
We need a breakthrough in chemistry and the ability to solve the Schrodinger equation in 3D (Or something similar) to truly solve this. Ie generate the electronic structure statically without using arbitrary, tuned constants, then evolve it over time. We know the rules and constraints, but unfortunately, can't solve it without using approximations, and fitting to experimental data.
Machine-learning approaches will always suffer from over-fitting; they can produce practical results for known cases, but their predictive power is limited. (But still impressive!)
A group of incredibly intelligent people allowed to do whatever fundamental research they want at the expense of parent company's business, whose value will not be appreciated for a couple decades until everything new in technology points back to a fundamental innovation coming out of that lab.
An analogy might be that if you trained an AI model on billiard balls, it could become really good at telling you where a ball will end up when you hit it, but it could never tell you that the reason is that f=m*a, meaning it will do nothing to advance the science.
https://www.alpco.com/dorothy-hodgkins-discovery-insulins-3d...
The OP didn't say that it is not useful, what they implied was that it is not actually science, which is correct. Science is a system that produces and organises knowledge. Chomsky made this point many years ago in a similar debate in linguistics. Statistical learning might produce results, but it tells us virtually nothing about the underlying laws or structures that govern language use.
ML in its current from is effectively the modern version of behaviourism and will, or already does, suffer from the same issues.
I guess you're hoping for some higher-level heuristic that would let us skip a lot of the computation. Maybe it exists. That isn't how nature does it, though.
That said I also believe that QC in principle could be a way to address this effectively as well, but I'm waiting until I see somebody demonstrate something interesting and useful before I get excited.
By analogy, billiards is nothing but highschool physics but understanding highschool physics does not on its own make you a master billiards player able to sink any shot.
In some sense, protein folding is a chemistry problem, in that it is entirely about determining the structure of (a very specific type of) molecules.
In another sense, protein folding is a computational problem that is a necessary input to answering higher level questions in the field of biology.
Put another way, this allows researchers in biology to not need to care about the science of protein folding.
When the human genome was sequenced another entrepreneur came in (Venter), said "You guys are morons to spend billions over decades, let me show how an actual smart team would do it" and bet them to it in a fraction of time and cost. Yet the consortium that spent billions on the human genome project still got congratulated. What's ironic is that when they said they sequenced the human genome they still had as many asterisks to that statement as this broad DeepMind statement has. In fact, the sequencing of the human genome claim was probably more disingenuous than DeepMind because, and this is important, they didn't make up this definition - the CASP organizers did. This team just met a pre defined standard as what's accepted as "solving of protein folding" and this brilliant team met this challenge.
Academics calling this hyperbole should fire every university's PR team because the amount of hyperbole they add to every press release about a paper where they "cured" cancer in mice is 10x larger than this.
Academia is fundamentally broken; the cracks started appearing in the sixties (read Hammings lecture notes), and have all but metastatasized throughout, especially in biology. We are all dying faster because of this (Google the Alzheimer's cabal). Academia is now just bunch of overperforming hacks who are honestly not that good at anything except sitting in circles in NIH grant review panels giving millions to each other while giving "constructive criticism" like this to what is clearly a monumental achievement, because if they don't then they reinforced how useless they are together as a group.
Going back to this hacky article, of course this is the first step, and there is much to be ironed out. But in the words of Sydney Brenner, a great scientist from a time when actually smart, humble people became professors, "The entry of large numbers of American ... into the field will ensure that all the chemical details ... will be elucidated." [1]
We are definitely now in a fairly deterministic path towards figuring out protein folding for all practical purposes. The few academics who still have humility and foresight see this. DeepMind is obligated to release enough results and there are still some sane minds left in academia that they will do what they are okay at, which is filling in the details.
[1] http://nemaplex.ucdavis.edu/General/Biographies/SBrenner.htm
I was in my org conference and the guy that head AI team was stating how Deep Learning going to change the world. That it'll write software in a few years (he's from the applied math field). He also glossed over many on going problems with Deep Learning.
We're in healthcare industry and this guy is pushing Theranos like level of snake oil.
The people that don't know a lick about ML or Statistic or both rely on these people. The doctors are relying on that dude and I for these inputs. And the dude is selling theranos like stuff. I think the problem is the medical field, cs, and stat are all high level field, requiring years of training to acquire mastery and knowledge. So it's rare that someone would have all three to be able to have a impartial view or a good overview of pros and cons.