AlphaFold 3 predicts the structure and interactions of all of life's molecules

If you're a scientist who works in protein folding (or one of those other areas) and strongly believe that science's goal is to produce falsifiable hypotheses, these new approaches will be extremely depressing, especially if you aren't proficient enough with ML to reproduce this work in your own hands.

If you're a scientist who accepts that probabilist models beat interpretable ones (articulated well here: https://norvig.com/chomsky.html), then you'll be quite happy because this is yet another validation of the value of statistical approaches in moving our ability to predict the universe forward.

If you're the sort of person who believes that human brains are capable of understanding the "why" of how things work in all its true detail, you'll find this an interesting challenge- can we actually interpret these models, or are human brains too feeble to understand complex systems without sophisticated models?

If you're the sort of person who likes simple models with as few parameters as possible, you're probably excited because developing more comprehensible or interpretable models that have equivalent predictive ability is a very attractive research subject.

(FWIW, I'm in the camp of "we should simultaneously seek simpler, more interpretable models, while also seeking to improve native human intelligence using computational augmentation")

It means we now have an accurate surrogate model or "digital twin" that can be experimented on almost instantaneously. So we can massively accelerate the traditional process of developing mechanistic understanding through experiment, while also immediately be able to benefit from the ability to make accurate predictions, even without needing understanding.

In reality, science has already pretty much gone this way long ago, even if people don't like to admit it. Simple, reductionist explanations for complex phenomena in living systems don't really exist. Virtually all of medicine nowadays is empirical: try something, and if you can prove its safe and effective, you keep doing it. We almost never have a meaningful explanation for how it really works, and when we think we do, it gets proven wrong repeatedly, while the treatment keeps working as always.

It depends whether the value of science is human understanding or pure prediction. In some realms (for drug discovery, and other situations where we just need an answer and know what works and what doesn’t), pure prediction is all we really need. But if we could build an uninterpretable machine learning model that beats any hand-built traditional ‘physics’ model, would it really be physics?

Maybe there’ll be an intermediate era for a while where ML models outperform traditional analytical science, but then eventually we’ll still be able to find the (hopefully limited in number) principles from which it can all be derived. I don’t think we’ll ever find that Occam’s razor is no use to us.

It makes me think about how Einstein was famous for making falsifiable real-world predictions to accompany his theoretical work. And, sometimes it took years for proper experiments to be run (such as measuring a solar eclipse during the breakout of a world war).

Perhaps the opportunity here is to provide a quicker feedback loop for theory about predictions in the real world. Almost like unit tests.

Does this decouple progress from our current understanding of the scientific process?

Thank God! As a person who uses my brain, I think I can say, pretty definitively, that people are bad at understanding things.

If this actually pans out, it means we will have harnessed knowledge/truth as a fundamental force, like fire or electricity. The "black box" as a building block.

The most moneyed and well-coordinated organizations have honed a large hammer, and they are going to use it for everything, and so almost certainly future big findings in the areas you mention, probabilistically inclined models coming from ML will be the new gold standard.

But yet the only thing that can save us from ML will be ML itself because it is ML that has the best chance to be able to extrapolate patterns from these blackbox models to develop human interpretable models. I hope we do dedicate explicit effort to this endeavor, and so continue the human advances and expanse of human knowledge in tandem with human ingenuity with computers at our assistance.

My argument is: weather.

I think it is fine & better for society to have applications and models for things we don't fully understand... We can model lots of small aspects of weather, and we have a lot of factors nailed down, but not necessarily all the interactions.. and not all of the factors. (Additional example for the same reason: Gravity)

Used responsibly. Of course. I wouldn't think an AI model designing an airplane that no engineers understand how it works is a good idea :-)

And presumably all of this is followed by people trying to understand the results (expanding potential research areas)

I wonder if ML can someday be employed in deciphering such black box problems; a second model that can look under the hood at all the number crunching performed by the predictive model, identify the pattern that resulted in a prediction, and present it in a way we can understand.

That said, I don’t even know if ML is good at finding patterns in data.

Stepping back, the high-order bit here is an ML method is beating physically-based methods for accurately predicting the world.

I mean, it's just faster, no? I don't think anyone is claiming it's a more _accurate_ model of the universe.

We should be thankful that we live in the universe that obeys math simple enough to comprehend that we were able to reach that level.

Imagine if optis was complex enough that it would require ML model to predict anything.

We'd be in permanent stone age without a way out.

As a steelman, wouldn't the abundance of infinitely generate-able situations make it _easier_ for us to develop strong theories and models? The bottleneck has always been data. You have to do expensive work in the real world and accurately measure it before you can start fitting lines to it. If we were to birth an e.g. atomically accurate ML model of quantum physics, I bet it wouldn't take long until we have mathematical theories that explain why it works. Our current problem is that this stuff is super hard to manipulate and measure.

I would assume that given enough hints from AI and if it is deemed important enough humans will come in to figure out the “first principles” required to arrive at the conclusion.

I asked a friend of mine who is chemistry professor at a large research university something along these lines a while ago. He said that so far these models don't work well in regions where either theory or data is scarce, which is where most progress happens. So he felt that until they can start making progress in those areas it won't change things much.

For me the big question is how do we confidently validate the output of this/these model(s).

Many of our existing physical models can be decomposed into "high-confidence, well tested bit" plus "hand-wavy empirically fitted bit". I'd like to see progress via ML replacing the empirical part - the real scientific advancement then becomes steadily reducing that contribution to the whole by improving the robust physical model incrementally. Computational performance is another big influence though. Replacing the whole of a simulation with an ML model might still make sense if the model training is transferrable and we can take advantage of the GPU speed-ups, which might not be so easy to apply to the foundational physical model solution. Whether your model needs to be verified against real physical models depends on the seriousness of your use-case; for nuclear weapons and aerospace weather forecasts I imagine it will remain essential, while for a lot of consumer-facing things the ML will be good enough.

This is the topic of epistemology of the sciences in books such as "New Direction in the Philosophy of Mathematics" [1] and happened before with problems such as the four color theorem [2] where AI was not involved.

Going back to the uninterpretable ML models in the context of AlphaFold 3, I think one method for trying to explain the findings is similar to the experimental methods of physics with reality: you perform experiments with the reality (in this case AlphaFold 3) to came up with sound conclusions. AI/ML is an interesting black-box system.

There are other open discussions on this topic. For example, can our human brain absorbe that knowledge or it is limited somehow with the scientific language that we have now?

[1] https://www.google.com.ar/books/edition/New_Directions_in_th...

[2] https://en.wikipedia.org/wiki/Four_color_theorem

No, science doesn't work that way. You can just calculate your way to scientific discoveries, you got to test them in the real world. Learning, both in humans and AI, is based on the signals provided by the environment. There are plenty of things not written anywhere, so the models can't simply train on human text to discover new things. They learn directly from the environment to do that, like AlphaZero did when it beat humans at Go.

You are conflating the whole scientific endeavor to a very specific problem to which this specific approach is effective at producing results that fit with the observable world. This has nothing to do with science as a whole.

In case it's not clear, this does not "beat" experimental structure determination. The matches to experiment are pretty close, but they will be closer in some cases than others and may or may not be close enough to answer a given question about the biochemistry. It certainly doesn't give much information about the dynamics or chemical perturbations that might be relevant in biological context. That's not to pooh-pooh alphafold's utility, just that it's a long way from making experimental structure determination unnecessary, and much much further away from replacing a carefully chosen scientific question and careful experimental design.

A few things:

1. Research can then focus on where things go wrong

2. ML models, despite being "black boxes," can still have brute-force assessment performed of the parameter space over covered and uncovered areas by input information

3. We tend to assume parsimony (i.e Occam's razor) to give preference to simpler models when all else is equal. More complex black-box models exceeding in prediction let us know the actual causal pathway may be more complex than simple models allow. This is okay too. We'll get it figured out. Not everything is closed-form, especially considering quantum effects may cause statistical/expected outcomes instead of deterministic outcomes.

I suspect that ML will be state-of-the-art at generating human-interpretable theories as well. Just a matter of time.

There will be an iterative process built around curated training datasets - continually improved, top tier models, teams reverse engineering the model's understanding and reasoning, and applying that to improve datasets and training.

The ML models will help us understand that :)

Reminds me of the novel Blindsight - in it there's special individuals who work as synthesists, whos job it is to observe and understand and then somehow translate back to "lay person" the seemingly undecipherable actions/decisions of advanced computers and augmented humans.

Our metaphors and intuitions were crumbling already and stagnating. See quantum physics: sometimes a particle, sometimes a wave, and what constitute a measurement anyway?

I’ll take prediction over understanding if that’s the best our brains can do. We’ve evolved to deal with a few orders of magnitude around a meter and a second. Maybe dealing with light-years and femtometer/seconds is too much to ask.

A new-ish field of "mechanistic interpretability" is trying to poke at weights and activations and find human-interpretable ideas w/in them. Making lots of progress lately, and there are some folks trying to apply ideas from the field to Alphafold 2. There are hopes of learning the ideas about biology/molecular interactions that the model has "discovered".

Perhaps we're in an early stage of Ted Chiang's story "The Evolution of Human Science", where AIs have largely taken over scientific research and a field of "meta-science" developed where humans translate AI research into more human-interpretable artifacts.

It's interesting to compare this situation to earlier eras in science. Newton, for example, gave us equations that were very accurate but left us with no understanding at all of why they were accurate.

It seems like we're repeating that here, albeit with wildly different methods. We're getting better models but by giving up on the possibility of actually understanding things from first principles.

Is alphafold doing model generation or is it just reducing a massive state space?

The current computational and systems biochemistry approaches struggle to model large biomolecules and their interactions due to the large degrees of freedom of the models.

I think it is reasonable to rely on statistical methods to lead researchers down paths that have a high likelihood of being correct versus brute forcing the chemical kinetics.

After all chemistry is inherently stochastic…

Science has always given us better, but error prone tooling to see further and make better guesses. There is still a scientific test. In a clinical trial, is this new drug safe and effective.

Some machine learning models might be more interpretable than others. I think the recent "KAN" model might be a step forward.

I believe it simply tells us that our understanding of mechanical systems, especially chaotic ones, is not as well defined as we thought.

https://journals.aps.org/prresearch/abstract/10.1103/PhysRev...

That is not a real concern, just a confusion on how statistics works :(

We will get better with understanding black boxes, if a model can be compressed into simple math formula then it's both easier to understand and to compute.

I think it likely that instead of replacing existing methods, we will see a fusion. Or rather, many different kinds of fusions - depending on the exact needs of the problems at hand (or in science, the current boundary of knowledge). If nothing else then to provide appropriate/desirable level of explainability, correctness etc. Hypothetically the combination will also have better predictive performance and be more data efficient - but it remains to be seen how well this plays out in practice. The field of "physics informed machine learning" is all about this.

These processes are both beyond human comprehension because they contain vast layers of tiny interactions and also not practical to simulate. This tech will allow for exploration for accurate simulations to better understand new ideas if needed.

every time the two systems disagree, it's an opportunity to learn something. both kinds of models can be improved with new information, done through real-world experiments

I think at some point, we will be able to produce models that are able to pass data into a target model and observe its activations and outputs and put together some interpretable pattern or loose set of rules that govern the input-output relationship in the target model. Using this on a model like AlphaFold might enable us to translate inferred chemical laws into natural language.

I can only hope the models will be sophisticated enough and willing to explain their reasoning to us.

Whatever it is if we needed to we could follow each instruction through the black box. It’s never going to be as opaque as something organic.

"better and better models of the world" does not always mean "more accurate" and never has.

We already know how to model the vast majority of things, just not at a speed and cost which makes it worthwhile. There are dimensions of value - one is accuracy, another speed, another cost, and in different domains additional dimensions. There are all kinds of models used in different disciplines which are empirical and not completely understood. Reducing things to the lowest level of physics and building up models from there has never been the only approach. Biology, geology, weather, materials all have models which have hacks in them, known simplifications, statistical approximations, so the result can be calculated. It's just about choosing the best hacks to get the best trade off of time/money/accuracy.

This is a neat observation. Slightly terrifying, but still interesting. Seems like there will also be cases where we discover new theories through the uninterpretable models—much easier and faster to experiment endlessly with a computer.

We need to advance mechanistic interpretability (field reverse engineering neural networks) https://www.youtube.com/watch?v=P7sjVMtb5Sg https://www.youtube.com/watch?v=7t9umZ1tFso https://www.youtube.com/watch?v=2Rdp9GvcYOE

What happens when...

I can only assume that existing methods would still be used for verification. At least we understand the logic used behind these methods. The ML models might become more accurate on average but they could still throw out results that are way off occasionally, so their error rate would have to become equal to the existing methods.

This is exactly how the physicists felt at the dawn of quantum physics - the loss of meaningful human inquiry to blindly effective statistics. Sobering stuff…

Personally, I’m convinced that human reason is less pure than we think it to be, and that the move to large mathematical models might just be formalizing a lack-of-control that was always there. But that’s less of a philosophy of science discussion and more of a cognitive science one

What happens when the best methods for computational fluid dynamics, molecular dynamics, nuclear physics are all uninterpretable ML models?

A better analogy is "weather forecasting".

In physics, we already deal with the fact that many of the core equations cannot be analytically solved for more than the most basic scenarios. We've had to adapt to using approximation methods and numerical methods. This will have to be another place where we adapt to a practical way of getting results.

The top HN response to this should be,

what happens is an opportunity has entered the chat.

There is a wave coming—I won't try to predict if it's the next one—where the hot thing in AI/ML is going to be profoundly powerful tools for analyze other such tools and render them intelligible to us,

which will I imagine mean providing something like a zoomable explainer. At every level there are footnotes; if you want to understand why the simplified model is a simplification, you look at the fine print. Which has fine print. Which has...

Which doesn't mean there is not a stable level at which some formal notion of "accurate" cannot be said to exist, which is the minimum viable level of simplification.

Etc.

This sort of thing will of course will the input to many other things.

We could be entering a new age of epicycles - high accuracy but very flawed understanding.

Perhaps for understanding the structure itself, but having the structure available allows us to focus on a coarser level. We also don't want to use quantum mechanics to understand the everyday world, and that's why we have classic mechanics etc.

The frontier in model space is kind of fluid. It's all about solving differential equations.

In theoretical physics, you know the equations, you solve equations analytically, but you can only do that when the model is simple.

In numerical physics, you know the equations, you discretize the problem on a grid, and you solve the constraint defined by the equations with various numerical integration schemes like RK4, but you can only do that when the model is small and you know the equations, and you find a single solution.

Then you want the result faster, so you use mesh-free methods and adaptive grids. It works on bigger models but you have to know the equations, finding a single solution to the differential equations.

Then you compress this adaptive grid with a neural network, while still knowing the governing equations, and you have things like Physics Informed Neural Networks ( https://arxiv.org/pdf/1711.10561 and following papers) where you can bound the approximation error. This method allows solve all solutions to the differential equations simultaneously, sharing the computations.

Then when knowing explicitly your governing equations is too complex, so you assume that there are some governing stochastic equations implicitly, which you learn the end-result of the dynamic with a diffusion model, that's what this alpha-fold is doing.

ML is kind of a memoization technique, analog to hashlife in the game of life, that allows you reuse your past computational efforts. You are free to choose on this ladder which memory-compute trade-off you want to use to model the world.

Might be easier to come up with new models with analytic solutions if you have a probabilistic model at hand. A lot easier to evaluate against data and iterate. Also, I wouldn't be surprised if we develop better tools for introspecting these models over time.

Interesting times indeed. I think the early history of medicines takes away from your observation though. In the 19th and early 20th century people didn't know why medicines worked, they just did. The whole "try a bunch of things on mice, pick the best ones and try them on pigs, and then the best of those and try a few on people" kind of thing. In many ways the mice were a stand in for these models, at the time scientists didn't understand nearly as much about how mice worked (early mice models were pretty crude by today's standards) but they knew they were a close enough analog to the "real thing" that the information provided by mouse studies was usefully translated into things that might help/harm humans.

So when you're tools can produce outputs that you find useful, you can then use those tools to develop your understanding and insights. As a tool, this is quite good.

"Best methods" is doing a lot of heavy lifting here. "Best" is a very multidimensional thing, with different priorities leading to different "bests." Someone will inevitably prioritize reliability/accuracy/fidelity/interpretability, and that's probably going to be a significant segment of the sciences. Maybe it's like how engineers just need an approximation that's predictive enough to build with, but scientists still want to understand the underlying phenomena. There will be an analogy to how some people just want an opaque model that works on a restricted domain for their purposes, but others will be interested in clearer models or unrestricted/less restricted domain models.

It could lead to a very interesting ecosystem of roles.

Even if you just limit the discussion to using the best model of X to design a better Y, limited to the model's domain of validity, that might translate the usage problem to finding argmax_X of valueFunction of modelPrediction of design of X. In some sense a good predictive model is enough to solve this with brute force, but this still leaves room for tons of fascinating foundational work. Maybe you start to find that the (wow so small) errors in modelPrediction are correlated with valueFunction, so the most accurate predictions don't make it the best for argmax (aka optimization might exploit model errors rather than optimizing the real thing). Or maybe brute force just isn't computationally feasible, so you need to understand something deeper about the problem to simplify the optimization to make it cheap.

Also no commercial use, from the paper:

AlphaFold 3 will be available as a non-commercial usage only server at https://www.alphafoldserver.com, with restrictions on allowed ligands and covalent modifications. Pseudocode describing the algorithms is available in the Supplementary Information. Code is not provided.

That sucks a bit. I was just wondering why they are touting that 3rd party company in their own blog post, who commercialise research tools, as well. Maybe there are some corporate agreements with them that prevents them from opening the system...

Imagine the goodwill for humanity for releasing these pure research systems for free. I just have a hard time understanding how you can motivate to keep it closed. Let's hope it will be replicated by someone who doesn't have to hide behind the "responsible AI" curtain as it seems they are now.

Are they really thinking that someone who needs to predict 11 structures per day are more likely to be a nefarious evil protein guy than someone who predicts 10 structures a day? Was AlphaFold-2 (that was open-sourced) used by evil researchers?

The second amendment prevents the government's overreaching perversion to restrict me from having the ability to print biological weapons from the comfort of my couch.

Google has no such restriction.

Or OpenFold, which is the more literal reproduction of AlphaFold 2: https://github.com/aqlaboratory/openfold

in other words, this has been converted to a novelty, and has no use for scientific purposes.

Not just unfortunate, but doesn't this make it completely untrustable? How can you be sure the data was not modified in any way? How can you verify any results?

The AI call is rolling fast, I see similarities with cryptography in the 90s.

I have a history to tell for the record, back in the 90s we developed a home banking for Palm (with a modem), it was impossible to perform RSA because of the speed so I contacted the CEO of Certicom which was the unique elliptic curve cryptography implementation at that time. Fast forward and ECC is everywhere.

Well, it's because you can design deadly viruses using this technology. Viruses gain entry to living cells via cell-surface receptor proteins whose normal job is to bind signalling molecules, alter their conformation and translate that external signal into the cellular interior where it triggers various responses from genomic transcription to release of other signal molecules. Viruses hijack such mechanisms to gain entry to cells.

Thus if you can design a viral coat protein to bind to a human cell-surface receptor, such that it gets translocated into the cell, then it doesn't matter so much where that virus came from. The cell's firewall against viruses is the cell membrane, and once inside, the biomolecular replication machinery is very similar from species to species, particularly within restricted domains, such as all mammals.

Thus viruses from rats, mice, bats... aren't going to have major problems replicating in their new host - a host they only gained access to because some nation-state actors working in collaboration on such gain-of-function research in at least two labs on opposite sides of the world with funds and material provided by the two largest economic powers for reasons that are still rather opaque, though suspiciously banal...

Now while you don't need something like AlphaFold3 to do recklessly stupid things (you could use directed evolution, making millions of mutatad proteins, throwing them at a wall of human cell receptors and collecting what stuck), it makes it far easier. Thus Google doesn't want to be seen as enabling, though given their prediliction for classified military-industrial contracting to a variety of nation-states, particularly with AI, with revenue now far more important than silly "don't be evil" statements, they might bear watching.

On the positive side, AlphaFold3 will be great for fields like small molecular biocatalysis, i.e. industrial applications in which protein enzymes (or more robust heterogenous catalysts designed based on protein structures) convert N2 to ammonia, methane to methanol, or selectively bind CO2 for carbon capture, modification of simple sugars and amino acids, etc.

The logical consequence is to put all scientific publications under a license that restricts the right to train commercial ai models on them.

Science advances because of an open exchange of ideas, the original idea of patents was to grant the inventor exclusive use in exchange for disclosure of knowledge.

Those who did not patent, had to accept that their inventions would be studied and reverse engineered.

The „as a service“ model, breaks that approach.

This turns it into a tool that deserves to be dethroned by another group, frankly. What a strange choice.

Accuracy can be assessed two main ways: computationally and experimentally. Computationally, they would compare the predicted structures and interactions with known data from databases like PDB (Protein Database). Experimentally, they can use tools like x-ray crystallography and NMR (nuclear magnetic resonance) to obtain the actual molecule structure and compare it to the predicted result. The outcomes of each approach would be fed back into the model for refining future predictions.

https://www.rcsb.org/

The alphafold work has been used across the industry (successfully, in the sense of blind prediction), and has been replicated independently. The work on alphafold will likely net Demis and John a Nobel prize in the next few years.

(that said, one should always inspect Google publications with a fine-toothed comb and lots of skepticism, as they have a tendency to juice the results)

We have yet to find any strikingly novel compounds in the GNoME and Stable Structure listings, although we anticipate that there must be some among the 384,870 compositions. We also note that, while many of the new compositions are trivial adaptations of known materials, the computational approach delivers credible overall compositions, which gives us confidence that the underlying approach is sound.

Doesn't seem outright useless.

Are you an editor or reviewer?

Nature has long been willing to break its own rules to be at the forefront of publishing new science.

Syntax error

The title OP gave accurately reflects the title of Google's blog post. Title should not be editorialized.

A prediction is a prediction; it's not necessarily a correct prediction.

The weatherman predicts the weather, even if he's sometimes wrong, we don't say "he attempts to predict" the weather.

AlphaFold has been widely validated- it's now appreciated that its predictions are pretty damn good, with a few important exceptions, instances of which are addressed with the newer implementation.

So the criticism towards AlphaFold 2 will likely still apply? For example, it’s more accurate for predicting structures similar to existing ones, and fails at novel patterns?

Yes, and there is simply no way to bridge that gap with this technique. We can make it better and better at pattern matching, but it is not going to predict novel folds.

problem is biomolecules, are "chaperoned" to fold properly, only specific regions such as, alpha helix, or beta pleatedsheet will fold de novo.

Chaperone (protein)

https://en.wikipedia.org/wiki/Chaperone_(protein)

I am not aware of anybody currently criticiszing AF2's abilities outside of its training set. In fact the most recent papers (written by crystallographers) they are mostly arguing about atomic-level details of side chains at this point.

the R&D of all major pharma is currently using AlphaFold predictions when they don't have experimentally determined structures. I cannot share further details but the results suggest that we will see future pharmaceuticals based on AF predictions.

The important thing to recognize is that protein structures are primarily hypothesis-generation machines and tools to stimulate ideas, rather that direct targets of computational docking. Currently structures rarely capture the salient details required to identify a molecule that has precisely the biological outcome desired, because the biological outcome is an extremely complex function that incorporates a wide array of other details, such as other proteins, metabolism, and more.

There are a few AI-designed drugs in various phases of clinical trials, these things take time.

as soon as google tries to think commercially this will shut down so the longer it stays pure research the better. google is bad with productization.

I wonder what the license for RoseTTAFold is. On github you have:

https://github.com/RosettaCommons/RoseTTAFold/blob/main/LICE...

But there's also:

https://files.ipd.uw.edu/pub/RoseTTAFold/Rosetta-DL_LICENSE....

Which is it?

That's pretty good. Based on the previous performance improvements of Alpha-- models, it'll be nearing 100% in the next couple of years.

Marketing vs. Reality :)

I don't blame them for hyping their products - if only to fight the sentiment that Google is far behind OpenAI because they were not first to release a LLM.

Thanks for this informative video summary. As a layperson, with a BS in Chemistry, it was quite helpful in understanding main bulletpoints of this accomplishment.

Consider that humans also learn from other humans, and sometimes surpass their teachers.

A bit more comfortable?