28 ms·

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

1100 points by zerojames 2y ago

s1artibartfast 2y ago
The article was heavy on the free research aspect, but light on the commercial application. I'm curious about the business strategy. Does Google intend to license out tools, partner, or consult for commercial partners?
- ilrwbwrkhv 2y ago
  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.
  - s1artibartfast 2y ago
    I don't think it was ever pure research. The article talks about infinity labs, which is the co. Mercial branch for drug discovery. I do agree that Google seems bad at commercialization, which is why I'm curious on what the strategy is. It is hard to see them being paid consultants or effective partners for pharma companies, let alone developing drugs themselves.
- throwtappedmac 2y ago
  [flagged]
- candiodari 2y ago
  I wonder what the license for RoseTTAFold is. On github you have: https://github.com/RosettaCommons/RoseTTAFold/blob/main/LICENSE https://github.com/RosettaCommons/RoseTTAFold/blob/main/LICE... But there's also: https://files.ipd.uw.edu/pub/RoseTTAFold/Rosetta-DL_LICENSE.txt https://files.ipd.uw.edu/pub/RoseTTAFold/Rosetta-DL_LICENSE.... Which is it?
- a_bonobo 2y ago
  This version has Isomorphic Labs far more in the focus of the press release, which seems to be now the commercial arm more or less licensing access out. The new AlphaFold server does not do everything the paper says AlphaFold 3 says it does. You cannot predict docking with the server! That is the main interest of pharma companies, 'does our medication bind to the target protein?'. From the FAQ: 'AlphaFold Server is a web-service that offers customized biomolecular structure prediction. It makes several newer AlphaFold3 capabilities available, including support for a wider range of molecule type' - that's not ALL AlphaFold3 capabilities. Isomorphic prints the money with those additional capabilities. It's hilarious that Google says they don't allow this for safety reasons, pure OpenAI fluff. It's just money.
weregiraffe 2y ago
s/predicts/attempts to predict
- jasonjmcghee 2y ago
  The title OP gave accurately reflects the title of Google's blog post. Title should not be editorialized.
  - jtbayly 2y ago
    Unless the title is clickbait, which it appears this is…
- matt-attack 2y ago
  Syntax error
  - adrianmonk 2y ago
    Legal without the trailing slash in vi!
- pbw 2y ago
  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.
- dekhn 2y ago
  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.
  - AtlasBarfed 2y ago
    "pretty damn good" So... what percentage of the time? If you made an AI to pilot an airplane, how would you verify its edge conditions, you know, like plummeting out of the sky because it thought it had to nosedive? Because these AIs are black box neural networks, how do you know they are predicting things correctly for things that aren't in the training dataset? AI has so many weasel words.
Metacelsus 2y ago
From: https://www.nature.com/articles/d41586-024-01383-z https://www.nature.com/articles/d41586-024-01383-z >Unlike RoseTTAFold and AlphaFold2, scientists will not be able to run their own version of AlphaFold3, nor will the code underlying AlphaFold3 or other information obtained after training the model be made public. Instead, researchers will have access to an ‘AlphaFold3 server’, on which they can input their protein sequence of choice, alongside a selection of accessory molecules. [. . .] Scientists are currently restricted to 10 predictions per day, and it is not possible to obtain structures of proteins bound to possible drugs. This is unfortunate. I wonder how long until David Baker's lab upgrades RoseTTAFold to catch up.
- wslh 2y ago
  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.
- l33tman 2y ago
  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?
  - staminade 2y ago
    Isomorphic Labs? That's an Alphabet owned startup run by Denis Hassabis that they created to commercialise the Alphafold work, so it's not really a 3rd party at all.
renonce 2y ago
> What is different about the new AlphaFold3 model compared to AlphaFold2? > AlphaFold3 can predict many biomolecules in addition to proteins. AlphaFold2 predicts structures of proteins and protein-protein complexes. AlphaFold3 can generate predictions containing proteins, DNA, RNA, ions,ligands, and chemical modifications. The new model also improves the protein complex modelling accuracy. Please refer to our paper for more information on performance improvements. AlphaFold 2 generally produces looping “ribbon-like” predictions for disordered regions. AlphaFold3 also does this, but will occasionally output segments with secondary structure within disordered regions instead, mostly spurious alpha helices with very low confidence (pLDDT) and inconsistent position across predictions. 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?
- deleted 2y ago
  [deleted]
- dekhn 2y ago
  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.
- COGlory 2y ago
  >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.
  - dekhn 2y ago
    alphafold has been shown to accurately predict some novel folds. The technique doesn't entirely depend on whole-domain homology.
  - flobosg 2y ago
tea-coffee 2y ago
This is a basic question, but how is the accuracy of the predicted biomolecular interactions measured? Are the predicted interactions compared to known interactions? How would the accuracy of predicting unknown interactions be assessed?
- joshuamcginnis 2y ago
  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/ https://www.rcsb.org/
  - dekhn 2y ago
    AlphaFold very explicitly (unless something has changed) removes NMR structures as references because they are not accurate enough. I have a PhD in NMR biomolecular structure and I wouldn't trust. the structures for anything.
    - JackFr 2y ago
      Sorry, I don’t mean to be dense - do you mean you don’t trust AlphaFolds structures or NMRs?
      - dekhn 2y ago
        I don't trust NMR structures in nearly all cases. The reasons are complex enough that I don't think it's worthwhile to discuss on Hacker News.
        fikama 2y ago
        Hmm, I would say its always worth to share knowledge. Could you paste some links or maybe type a few key-words for anyone willing to reasearch the topic further on his own.
dopylitty 2y ago
This reminds me of Google’s claim that another “AI” discovered millions of new materials. The results turned out to be a lot of useless noise but that was only apparent after actual expert spent hundreds of hours reviewed the results[0] 0: https://www.404media.co/google-says-it-discovered-millions-of-new-materials-with-ai-human-researchers/ https://www.404media.co/google-says-it-discovered-millions-o...
- dekhn 2y ago
  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)
  - 11101010001100 2y ago
    Depending on your expected value of quantum computing, the Nobel committee shouldn't wait too long.
    - dekhn 2y ago
      Personally I don't expect QC to be a competitor to ML in protein structure prediction for the foreseeable future. After spending more money on molecular dynamics than probably any other human being, I'm really skeptical that physical models of protein structures will compete with ML-based approaches (that exploit homology and other protein sequence similarities).
  - nybsjytm 2y ago
    >The alphafold work has been used across the industry (successfully, in the sense of blind prediction), and has been replicated independently. This is clearly an overstatement, or at least very incomplete. See for instance https://www.nature.com/articles/s41592-023-02087-4 https://www.nature.com/articles/s41592-023-02087-4: "In many cases, AlphaFold predictions matched experimental maps remarkably closely. In other cases, even very high-confidence predictions differed from experimental maps on a global scale through distortion and domain orientation, and on a local scale in backbone and side-chain conformation. We suggest considering AlphaFold predictions as exceptionally useful hypotheses."
_xerces_ 2y ago
A video summary of why this research is important: https://youtu.be/Mz7Qp73lj9o?si=29vjdQtTtIOk_0CV https://youtu.be/Mz7Qp73lj9o?si=29vjdQtTtIOk_0CV
- ProllyInfamous 2y ago
  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.
moconnor 2y ago
Stepping back, the high-order bit here is an ML method is beating physically-based methods for accurately predicting the world. What happens when the best methods for computational fluid dynamics, molecular dynamics, nuclear physics are all uninterpretable ML models? Does this decouple progress from our current understanding of the scientific process - moving to better and better models of the world without human-interpretable theories and mathematical models / explanations? Is that even iteratively sustainable in the way that scientific progress has proven to be? Interesting times ahead.
- jpadkins 2y ago
  Hook the protein model up to an LLM model, have the LLM interpret the results. Problem solved :-) Then we just have to trust the LLM is giving us correct interpretations.
- cgearhart 2y ago
  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.
- fnikacevic 2y ago
  I can only hope the models will be sophisticated enough and willing to explain their reasoning to us.
- thomasahle 2y ago
  > 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.
  - Jerrrry 2y ago
    Collision libraries and fluid libraries have had baked-in memorized look-up tables that were generated with ML methods nearly a decade ago. World is still here, although the Matrix/metaverse is becoming more attractive daily.
qwertox 2y ago
> Thrilled to announce AlphaFold 3 which can predict the structures and interactions of nearly all of life’s molecules with state-of-the-art accuracy including proteins, DNA and RNA. [1] There's a slight mismatch between the blog's title and Demis Hassabis' tweet, where he uses "nearly all". The blog's title suggests that it's a 100% solved problem. [1] https://twitter.com/demishassabis/status/1788229162563420560 https://twitter.com/demishassabis/status/1788229162563420560
- bmau5 2y ago
  Marketing vs. Reality :)
- TaupeRanger 2y ago
  First time reading a Deep Mind PR? This is literally their modus operandi.
- bamboozled 2y ago
  How to make the share price go up…surprised?
nybsjytm 2y ago
Important caveat: it's only about 70% accurate. Why doesn't the press release say this explicitly? It seems intentionally misleading to only report accuracy relative to existing methods, which apparently are just not so good (30%, 50% in various settings). https://www.fastcompany.com/91120456/deepmind-alphafold-3-dna-rna-modeling https://www.fastcompany.com/91120456/deepmind-alphafold-3-dn...
- bluerooibos 2y ago
  That's pretty good. Based on the previous performance improvements of Alpha-- models, it'll be nearing 100% in the next couple of years.
  - nybsjytm 2y ago
    Just "Alpha-- models" in general?? That's not a remotely reasonable way to reason about it. Even if it were, why should it stop DeepMind from clearly communicating accuracy?
    - dekhn 2y ago
      The way I think about this (specifically, deepmind not publishing their code or sharing their exact experimental results): advanced science is a game played by the most sophisticated actors in the world. Demis is one of those actors, and he plays the games those actors play better than anybody else I've ever seen. Those actors don't care much about the details of any specific system's accuracy: they care to know that it's possible to do this, and some general numbers about how well it works, and some hints what approaches they should take. And Nature, like other top journals, is more than willing to publish articles like this because they know it stimulates the most competitive players to bring their best games. (I'm not defending this approach, just making an observation)
      - nybsjytm 2y ago
        I think it's important to qualify that the relevant "game" is not advanced science per se; the game is business whose product is science. The aim isn't to do novel science; it's to do something which can be advertised as novel science. That isn't to cast aspersions on the personal motivations of Hassabis or any other individual researcher working there (which itself isn't to remove their responsibilities to public understanding); it's to cast aspersions on the structure that they're part of. And it's not to say that they can't produce novel or important science as part of their work there. And it's also not to say that the same tension isn't often present in the science world - but I think it's present to an extreme degree at DeepMind. (Sometimes the distinction between novel science and advertisably novel science is very important, as seems to be the case in the "new materials" research dopylitty linked to in these comments: here https://www.404media.co/google-says-it-discovered-millions-of-new-materials-with-ai-human-researchers/ https://www.404media.co/google-says-it-discovered-millions-o...)
deleted 2y ago
[deleted]
j7ake 2y ago
So it’s okay now to publish a computational paper with no code? I guess Nature’s reporting standards don’t apply to everyone. > A condition of publication in a Nature Portfolio journal is that authors are required to make materials, data, code, and associated protocols promptly available to readers without undue qualifications. > Authors must make available upon request, to editors and reviewers, any previously unreported custom computer code or algorithm used to generate results that are reported in the paper and central to its main claims. https://www.nature.com/nature-portfolio/editorial-policies/reporting-standards#availability-of-computer-code https://www.nature.com/nature-portfolio/editorial-policies/r...
- boxed 2y ago
  Are you an editor or reviewer?
  - HanClinto 2y ago
    Good question. Also makes me wonder -- where's the line? Is it reasonable to have "layperson" reviewers? Is it reasonable to think that regular citizens could review such content?
    - Kalium 2y ago
      I think you will find that for the vast, vast majority of scientific papers there is significant negative expected value to even attempting to have layperson reviewers. Bear in mind that we're talking about papers written by experts in a specific field aimed at highly technical communication with other people who are experts in the same field. As a result, the only people who can usefully review the materials are drawn from those who are also experts in the same field. For an instructive example, look up the seminal paper on the structure of DNA: https://www.mskcc.org/teaser/1953-nature-papers-watson-crick-wilkins-franklin.pdf https://www.mskcc.org/teaser/1953-nature-papers-watson-crick... Ask yourself how useful comments from someone who did not know what an X-ray is, never mind anything about organic chemistry, would be in improving the quality of research or quality of communication between experts in both fields.
    - 2y ago
ein0p 2y ago
I’m inclined to ignore such pr fluff until they actually demonstrate a _practical_ result. Eg. cure some form of cancer or some autoimmune disease. All this “prediction of structure” has been in the news for years, and it seems to have resulted in nothing practically usable IRL as far as I can tell. I could be wrong of course, I do not work in this field
- dekhn 2y ago
  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.
  - ein0p 2y ago
    Sure. If/when we see anything practical, that’ll be the right moment to pay attention. This is much like “quantum computing” where everyone who doesn’t know what it is is excited for some reason, and those that do know can’t even articulate any practical applications
    - dekhn 2y ago
      Feynman already articulated the one practical application for quantum computing: using it to simulate complex systems (https://www.optica-opn.org/home/articles/on/volume_11/issue_2/features/quantum_mechanical_computers/ https://www.optica-opn.org/home/articles/on/volume_11/issue_... and https://calteches.library.caltech.edu/1976/ https://calteches.library.caltech.edu/1976/ and https://s2.smu.edu/~mitch/class/5395/papers/feynman-quantum-1981.pdf https://s2.smu.edu/~mitch/class/5395/papers/feynman-quantum-... These approaches are now being explored but I haven't seen any smoking guns showing a QC-based simulation exceeding the accuracy of a classical computer for a reasonable investment. Folks have suggested other areas, such as logistics, where finding small improvements to the best approximations might give a company a small edge, and crypto-breaking, but there has been not that much progress in this area, and the approximate methods have been improving rapidly.
mchinen 2y ago
I am trying to understand how accurate the docking predictions are. Looking at the PoseBusters paper [1] they mention, they say they are 50% more accurate than traditional methods. DiffDock, which is the best DL based systems gets 30-70% depending on the dataset, and traditional gets 50-70%. The paper highlighted some issues with the DL-based methods and given that DeepMind would have had time to incorporate this into their work and develop with the PoseBusters paper in mind, I'd hope it's significantly better than 50-70%. They say 50% better than traditional so I expected something like 70-85% across all datasets. I hope a paper will appear soon to illuminate these and other details. [1] https://pubs.rsc.org/en/content/articlehtml/2024/sc/d3sc04185a https://pubs.rsc.org/en/content/articlehtml/2024/sc/d3sc0418...
_obviously 2y ago
[flagged]
dsign 2y ago
For a couple of years I've been expecting that ML models would be able to 'accelerate' bio-molecular simulations, using physics-based simulations as ground truth. But this seems to be a step beyond that.
- dekhn 2y ago
  When I competed in CASP 20 years ago (and lost terribly) I predicted that the next step to improve predictions would be to develop empirically fitted force fields to make MD produce accurate structure predictions (MD already uses empirically fitted force fields, but they are not great). This area was explored, there are now better force fields, but that didn't really push protein structure prediction forward. Another approach is fully differentiable force fields- the idea that the force field function itself is a trainable structure (rather than just the parameters/weights/constants) that can be optimized directly towards a goal. Also explored, produced some interesting results, but nothing that woudl be considered transformative. The field still generally believes that if you had a perfect force field and infinite computing time, you could directly recapitulate the trajectories of proteins folding (from fully unfolded to final state along with all the intermediates), but that doesn't address any practical problems, and is massively wasteful of resources compared to using ML models that exploit evolutionary information encoded in sequence and structures. In retrospect I'm pretty relieved I was wrong, as the new methods are more effective with far fewer resources.
xnx 2y ago
Very cool that anyone can login to https://golgi.sandbox.google.com/ https://golgi.sandbox.google.com/ and check it out
_akhe 2y ago
Google's Game of Life 3D: Spiral edition
uptownfunk 2y ago
Very sad to see they did not make it open source. When you have a technology that has the potential to be a gateway for drug development, to the cures of new diseases, and instead you choose to make it closed, it is a very huge disservice to the community at large. Sure, release your own product alongside it, but making it closed source does not help the scientific community upon which all these innovations were built. Especially if you have lost a loved one to a disease which this technology will one day be able to create cures for, it is very disappointing.
- falcor84 2y ago
  The closer it gets to enabling full drug discovery, the closer it also gets to enabling bioterrorism. Taking it to the extreme, if they had the theory of everything, I don't think I'd want it to be made available to the whole world as it is today. On a related note, I highly recommend The Talos Principle 2, which really made me think about these questions.
  - pythonguython 2y ago
    Any organization/country that has the ability to use a tool like this to create a bio weapon is already sophisticated enough to do bioterrorism today.
    - ramon156 2y ago
      Alright, but now picture this: it's now open to the masses, meaning an individual could probably even do it.
      - uptownfunk 2y ago
        The risks don't exceed what is already out there. If someone wants to do damage, especially in America, there are more than enough ways they can do it already. The technology should be made free. I also wonder how much the claims are being exaggerated and are marketing-speak vs. real results. Is there any benchmark for this that they have published?
nojvek 2y ago
So much hyperbole from recent Google releases. I wish they didn't hype AI so much, but I guess that's what people want to hear, so they say that.
- sangnoir 2y ago
  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.
LarsDu88 2y ago
As a software engineer, I kind of feel uncomfortable about this new model. It outperforms Alphafold 2 at ligand binding, but Alphafold 2 also had some more hardcoded and interpretable structural reasoning baked into the model architecture. There's so many things you can incorporate into a protein folding model such as structural constraints, rotational equivariance, etc, etc This new model simple does away with some of that, achieving greater results. And the authors simply use distillation from data outputted from Alphafold2 and Alphafold2-multimer to get those better results for those cases where you wind up with implausible results. You have to run all those previous models, and output their predictions to do the distillation to achieve a real end-to-end training from scratch for this new model! Makes me feel a bit uncomfortable.
- amitport 2y ago
  Consider that humans also learn from other humans, and sometimes surpass their teachers. A bit more comfortable?
  - Balgair 2y ago
    Ahh, but the new young master is able to explain their work and processes to the satisfaction of the old masters. In the 'Science' of our modern times it's a requirement to show your work (yes, yes, I know about the replication crisis and all that terrible jazz). Not being able to ascertain how and why the ML/AI is achieving results is not quite the same and more akin to the alchemists and sorcerers with their cyphers and hidden laboratories.
    - falcor84 2y ago
      > the new young master is able to explain their work and processes to the satisfaction of the old masters Yes, but it's one level deep - in general they wouldn't be able to explain their work to their master's master (note "science advances one funeral at a time").
    - hackerdood 2y ago
      I’ll add that specially when it comes to playing go, professionals who are at the peak of their ability can often find the best move at a given point but be unable to explain why beyond “it feels right” or “it looks right”.
roody15 2y ago
I wonder in the not too distant future if these AI predictions could be explained back into “humanized” understanding. Much like ChatGPT can simplify complex topics … cold the model in the future provide feedback to researchers why it is making this prediction?
- deleted 2y ago
  [deleted]
reliablereason 2y ago
Would be very useful if one they used it to predict the structure and interaction of the known variants to. Would be very helpful when predicting if a mutation on a protein would lead to loss of function for the protein.
mfld 2y ago
The improvement on predicting protein/RNA/ligand interactions might facilitate many commercially relevant use cases. I assume pharma and biotech will eagerly get in line to use this.
tonyabracadabra 2y ago
Very cool, and what’s cooler is this rap about alphafold3 https://heymusic.ai/blog/news/alphafold-3 https://heymusic.ai/blog/news/alphafold-3
wuj 2y ago
This tool reminds me that the human body functions much like a black box. While physics can be modeled with equations and constraints, biology is inherently probabilistic and unpredictable. We verify the efficacy of a medicine by observing its outcomes: the medicine is the input, and the changes in symptoms are the output. However, we cannot model what happens in between, as we cannot definitively prove that the medicine affects only its intended targets. In many ways, much of what we understand about medicine is based on observing these black-box processes, and this tool helps to model that complexity.
- bamboozled 2y ago
  I’d say it’s always been the case for medicine, when people first used medicines, the intention was never to fully understand what happens, just save a life, eliminate or reduce symptoms. Now we’ve built explainable systems like computers and software, we try to overlay that onto everything and it might not work. To quote Alan Watts, humans like to try square out wiggly systems because we’re not great and understanding wiggles.
- a_bonobo 2y ago
  Classic essay in this vein: >Can a biologist fix a radio? — Or, what I learned while studying apoptosis https://www.cell.com/cancer-cell/pdf/S1535-6108(02)00133-2.pdf https://www.cell.com/cancer-cell/pdf/S1535-6108(02)00133-2.p... >However, if the radio has tunable components, such as those found in my old radio (indicated by yellow arrows in Figure 2, inset) and in all live cells and organisms, the outcome will not be so promising. Indeed, the radio may not work because several components are not tuned properly, which is not reflected in their appearance or their connections. What is the probability that this radio will be fixed by our biologists? I might be overly pessimistic, but a textbook example of the monkey that can, in principle, type a Burns poem comes to mind. In other words, the radio will not play music unless that lucky chance meets a prepared mind.
lysozyme 2y ago
Probably worth mentioning that David Baker’s lab released a similar model (predicts protein structure along with bound DNA and ligands), just a couple of months ago, and it is open source [1]. It’s also worth remembering that it was David Baker who originally came up with the idea of extending AlphaFold from predicting just proteins to predicting ligands as well [2]. 1. https://github.com/baker-laboratory/RoseTTAFold-All-Atom https://github.com/baker-laboratory/RoseTTAFold-All-Atom 2. https://alexcarlin.bearblog.dev/generalized/ https://alexcarlin.bearblog.dev/generalized/ Unlike AlphaFold 3, which predicts only a small, preselected subset of ligands, RosettaFold All Atom predicts a much wider range of small molecules. While I am certain that neither network is up to the task of designing an enzyme, these are exciting steps. One of the more exciting aspects of the RosettaFold paper is that they train the model for predicting structures, but then also use the structure predicting model as the denoising model in a diffusion process, enabling them to actually design new functional proteins. Presumably, DeepMind is working on this problem as well.
- theGnuMe 2y ago
  And that tech just got $1b in funding.
  - LeanderK 2y ago
    can you expand? Who got 1bn of funding?
- refulgentis 2y ago
  I appreciated this, but it's probably worth mentioning: when you say AlphaFold 3, you're talking about AlphaFold 2. TFA announces AlphaFold 3. Post: "Unlike AlphaFold 3, which predicts only a small, preselected subset of ligands, RosettaFold All Atom predicts a much wider range of small molecules" TFA: "AlphaFold 3...*models large biomolecules such as proteins, DNA and RNA*, as well as small molecules, also known as ligands" Post: "they also use the structure predicting model as the denoising model in a diffusion process...Presumably, DeepMind is working on this problem as well." TFA: "AlphaFold 3 assembles its predictions using a diffusion network, akin to those found in AI image generators."
bbstats 2y ago
Zero-shot nearly beating trained catboost is pretty amazing.
thenerdhead 2y ago
A lot of accelerated article previews as of recently. Seems like humanity is making a lot of breakthroughs. This is nothing short of amazing for all those suffering from disease.
ak_111 2y ago
If you work in this space would be interested to know what material impact has alphafold caused in your workflow since its release 4 years ago?
lumb63 2y ago
Would anyone more familiar with the field be able to provide some cursory resources on the protein folding problem? I have a background in computer science and a half a background in biology (took two semesters of OChem, biology, anatomy; didn’t go much further).
MPSimmons 2y ago
Not sure why the first thing they point it at wouldn't be prions.
itissid 2y ago
Noob here. Can one make the following deduction: In transformer based architectures, where one typically uses variation of attention mechanism to model interactions, even if one does not consider the autoregressive assumption of the domain's "nodes"(amino acids, words, image patches), if the number of final states that nodes take eventually can be permuted only in a finite way(i.e. they have sparse interactions between them), then these architectures are efficient way of modeling such domains. In plain english the final state of words in a sentence and amino acids in a protein have only so many ways they can be arranged and transformers do a good job of modeling it. Also can one assume this won't do well for domains where there is, say, sensitivity to initial conditions, like chaotic systems like wheather where the # final states just explodes?
ricksunny 2y ago
I'm interested in how they measure accuracy of binding site identification and binding pose prediction. This was missing for the hitherto widely-used binding pose prediction tool Autodock Vina (and in silico binding pose tools in general). Despite the time I invested in learning & exercising that tool, I avoided using it for published research because I could not credibly cite its general-use accuracy. Is / will Alphafold 3 be citeable in the sense of "I have run Alphafold on this particular target of interest and this array of ligands, and have found these poses of X kJ/mol binding energy, and this is known to an accuracy of Y% because of Alphafold 3's training set results cited below'
- l33tman 2y ago
  I've never trusted those predicted binding energies. If you have predicted a ligand/protein complex and have high confidence in it and want to study the binding energy I really think you should do a full MD simulation, you can pull the ligand-protein complex apart and measure the change in free energy explicitly. Also, and this is an unfounded guess only, the problem of protein / ligand docking is quite a bit more complex than protein folding - there seems to be a finite set of overall folds used in nature, while docking a small ligand to a big protein with flexible sidechains and even flexible large-scale structures can have induced fits that are really important to know and estimate, and I'm just very sceptical that it's going to be possible to in a general fashion ever predict these accurately by the AI model with the limited training data. Though you just need some hints, then you can run MD sims on them to see what happens for real.
TaupeRanger 2y ago
So after 6 years of this "revolutionary technology", what we have to show for all the hype and breathless press releases is: ....another press release saying how "revolutionary" it is. Fantastic. Thanks DeepMind.
dev1ycan 2y ago
Excited but also it's been a fair bit now and I have yet to see something truly remarkable come out of this
zmmmmm 2y ago
So much of the talk about their "free server" seems to be trying to distract from the fact that they are not releasing the model. I feel like it's an important threshold moment if this gets accepted into scientific use without the model being available - reproducibility of results becomes dependent on the good graces of a single commercial entity. I kind of hope that like OpenAI it just spurs creation of equivalent open models that then actually get used.
nsoonhui 2y ago
Here's something that bugs me about ML: all we have is prediction and no explanation how we come to that prediction, ie: no deeper understanding on the underlying principles. So despite that we got a good match this time, how can we be sure that the match will be equally good next time? And how to use ML to predict structure that we have no baseline to start with or experimental result to benchmark ? In the absence of physics-like principles, How can we ever be sure that ML results next time is correct ?
- throwaway4aday 2y ago
  Speaking of physics, we should borrow the quote "Shut up and calculate" to describe the situation: it works so use it now and worry about the explanations later.
  - d0mine 2y ago
    Except the model is not open-source. You can't calculate anything.
- coriny 2y ago
  There is a biannual structural prediction contest called CASP [1], in which a set of newly determined structures is used to benchmark the prediction methods. Some of these structures will be "novel", and so can be used to estimate the performance of current methods on predicting "structure that we have no baseline to start with". CASP-style assessments are something that should done for more research fields, but it's really hard to persuade funders and researchers to put up the money and embargo the data as required. [1] https://en.wikipedia.org/wiki/CASP https://en.wikipedia.org/wiki/CASP
sidcool 2y ago
And google is giving the service for free. Pretty good.
gajnadsgjoas 2y ago
Can someone tell me what are the direct implication of this? I often see "helps with a drug design" but I'm too far from this industry and have never seen an example of such drugs
- Syzygies 2y ago
  We're this much closer to being hacked?
DF1PAW 2y ago
Does it also simulate prion (=misfolded structures) based diseases?
niwaniwaaa 2y ago
How can I get my outputs in PDB format? Can not?
deleted 2y ago
[deleted]

1100 points by zerojames 2y ago

The article was heavy on the free research aspect, but light on the commercial application. I'm curious about the business strategy. Does Google intend to license out tools, partner, or consult for commercial partners?

ilrwbwrkhv 2y ago
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.
- s1artibartfast 2y ago
  I don't think it was ever pure research. The article talks about infinity labs, which is the co. Mercial branch for drug discovery. I do agree that Google seems bad at commercialization, which is why I'm curious on what the strategy is. It is hard to see them being paid consultants or effective partners for pharma companies, let alone developing drugs themselves.
throwtappedmac 2y ago
[flagged]
candiodari 2y ago
I wonder what the license for RoseTTAFold is. On github you have: https://github.com/RosettaCommons/RoseTTAFold/blob/main/LICENSE https://github.com/RosettaCommons/RoseTTAFold/blob/main/LICE... But there's also: https://files.ipd.uw.edu/pub/RoseTTAFold/Rosetta-DL_LICENSE.txt https://files.ipd.uw.edu/pub/RoseTTAFold/Rosetta-DL_LICENSE.... Which is it?
a_bonobo 2y ago
This version has Isomorphic Labs far more in the focus of the press release, which seems to be now the commercial arm more or less licensing access out. The new AlphaFold server does not do everything the paper says AlphaFold 3 says it does. You cannot predict docking with the server! That is the main interest of pharma companies, 'does our medication bind to the target protein?'. From the FAQ: 'AlphaFold Server is a web-service that offers customized biomolecular structure prediction. It makes several newer AlphaFold3 capabilities available, including support for a wider range of molecule type' - that's not ALL AlphaFold3 capabilities. Isomorphic prints the money with those additional capabilities. It's hilarious that Google says they don't allow this for safety reasons, pure OpenAI fluff. It's just money.

weregiraffe 2y ago

s/predicts/attempts to predict

jasonjmcghee 2y ago
The title OP gave accurately reflects the title of Google's blog post. Title should not be editorialized.
- jtbayly 2y ago
  Unless the title is clickbait, which it appears this is…
matt-attack 2y ago
Syntax error
- adrianmonk 2y ago
  Legal without the trailing slash in vi!
pbw 2y ago
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.
dekhn 2y ago
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.
- AtlasBarfed 2y ago
  "pretty damn good" So... what percentage of the time? If you made an AI to pilot an airplane, how would you verify its edge conditions, you know, like plummeting out of the sky because it thought it had to nosedive? Because these AIs are black box neural networks, how do you know they are predicting things correctly for things that aren't in the training dataset? AI has so many weasel words.

Metacelsus 2y ago

From: https://www.nature.com/articles/d41586-024-01383-z https://www.nature.com/articles/d41586-024-01383-z >Unlike RoseTTAFold and AlphaFold2, scientists will not be able to run their own version of AlphaFold3, nor will the code underlying AlphaFold3 or other information obtained after training the model be made public. Instead, researchers will have access to an ‘AlphaFold3 server’, on which they can input their protein sequence of choice, alongside a selection of accessory molecules. [. . .] Scientists are currently restricted to 10 predictions per day, and it is not possible to obtain structures of proteins bound to possible drugs. This is unfortunate. I wonder how long until David Baker's lab upgrades RoseTTAFold to catch up.

wslh 2y ago
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.
l33tman 2y ago
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?
- staminade 2y ago
  Isomorphic Labs? That's an Alphabet owned startup run by Denis Hassabis that they created to commercialise the Alphafold work, so it's not really a 3rd party at all.

renonce 2y ago

> What is different about the new AlphaFold3 model compared to AlphaFold2? > AlphaFold3 can predict many biomolecules in addition to proteins. AlphaFold2 predicts structures of proteins and protein-protein complexes. AlphaFold3 can generate predictions containing proteins, DNA, RNA, ions,ligands, and chemical modifications. The new model also improves the protein complex modelling accuracy. Please refer to our paper for more information on performance improvements. AlphaFold 2 generally produces looping “ribbon-like” predictions for disordered regions. AlphaFold3 also does this, but will occasionally output segments with secondary structure within disordered regions instead, mostly spurious alpha helices with very low confidence (pLDDT) and inconsistent position across predictions. 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?

deleted 2y ago
[deleted]
dekhn 2y ago
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.
COGlory 2y ago
>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.
- dekhn 2y ago
  alphafold has been shown to accurately predict some novel folds. The technique doesn't entirely depend on whole-domain homology.
- flobosg 2y ago

tea-coffee 2y ago

This is a basic question, but how is the accuracy of the predicted biomolecular interactions measured? Are the predicted interactions compared to known interactions? How would the accuracy of predicting unknown interactions be assessed?

joshuamcginnis 2y ago
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/ https://www.rcsb.org/
- dekhn 2y ago
  AlphaFold very explicitly (unless something has changed) removes NMR structures as references because they are not accurate enough. I have a PhD in NMR biomolecular structure and I wouldn't trust. the structures for anything.
  - JackFr 2y ago
    Sorry, I don’t mean to be dense - do you mean you don’t trust AlphaFolds structures or NMRs?
    - dekhn 2y ago
      I don't trust NMR structures in nearly all cases. The reasons are complex enough that I don't think it's worthwhile to discuss on Hacker News.
      - fikama 2y ago
        Hmm, I would say its always worth to share knowledge. Could you paste some links or maybe type a few key-words for anyone willing to reasearch the topic further on his own.

dopylitty 2y ago

This reminds me of Google’s claim that another “AI” discovered millions of new materials. The results turned out to be a lot of useless noise but that was only apparent after actual expert spent hundreds of hours reviewed the results[0] 0: https://www.404media.co/google-says-it-discovered-millions-of-new-materials-with-ai-human-researchers/ https://www.404media.co/google-says-it-discovered-millions-o...

dekhn 2y ago
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)
- 11101010001100 2y ago
  Depending on your expected value of quantum computing, the Nobel committee shouldn't wait too long.
  - dekhn 2y ago
    Personally I don't expect QC to be a competitor to ML in protein structure prediction for the foreseeable future. After spending more money on molecular dynamics than probably any other human being, I'm really skeptical that physical models of protein structures will compete with ML-based approaches (that exploit homology and other protein sequence similarities).
- nybsjytm 2y ago
  >The alphafold work has been used across the industry (successfully, in the sense of blind prediction), and has been replicated independently. This is clearly an overstatement, or at least very incomplete. See for instance https://www.nature.com/articles/s41592-023-02087-4 https://www.nature.com/articles/s41592-023-02087-4: "In many cases, AlphaFold predictions matched experimental maps remarkably closely. In other cases, even very high-confidence predictions differed from experimental maps on a global scale through distortion and domain orientation, and on a local scale in backbone and side-chain conformation. We suggest considering AlphaFold predictions as exceptionally useful hypotheses."

_xerces_ 2y ago

A video summary of why this research is important: https://youtu.be/Mz7Qp73lj9o?si=29vjdQtTtIOk_0CV https://youtu.be/Mz7Qp73lj9o?si=29vjdQtTtIOk_0CV

ProllyInfamous 2y ago
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.

moconnor 2y ago

Stepping back, the high-order bit here is an ML method is beating physically-based methods for accurately predicting the world. What happens when the best methods for computational fluid dynamics, molecular dynamics, nuclear physics are all uninterpretable ML models? Does this decouple progress from our current understanding of the scientific process - moving to better and better models of the world without human-interpretable theories and mathematical models / explanations? Is that even iteratively sustainable in the way that scientific progress has proven to be? Interesting times ahead.

jpadkins 2y ago
Hook the protein model up to an LLM model, have the LLM interpret the results. Problem solved :-) Then we just have to trust the LLM is giving us correct interpretations.
cgearhart 2y ago
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.
fnikacevic 2y ago
I can only hope the models will be sophisticated enough and willing to explain their reasoning to us.
thomasahle 2y ago
> 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.
- Jerrrry 2y ago
  Collision libraries and fluid libraries have had baked-in memorized look-up tables that were generated with ML methods nearly a decade ago. World is still here, although the Matrix/metaverse is becoming more attractive daily.

qwertox 2y ago

> Thrilled to announce AlphaFold 3 which can predict the structures and interactions of nearly all of life’s molecules with state-of-the-art accuracy including proteins, DNA and RNA. [1] There's a slight mismatch between the blog's title and Demis Hassabis' tweet, where he uses "nearly all". The blog's title suggests that it's a 100% solved problem. [1] https://twitter.com/demishassabis/status/1788229162563420560 https://twitter.com/demishassabis/status/1788229162563420560

bmau5 2y ago
Marketing vs. Reality :)
TaupeRanger 2y ago
First time reading a Deep Mind PR? This is literally their modus operandi.
bamboozled 2y ago
How to make the share price go up…surprised?

nybsjytm 2y ago

Important caveat: it's only about 70% accurate. Why doesn't the press release say this explicitly? It seems intentionally misleading to only report accuracy relative to existing methods, which apparently are just not so good (30%, 50% in various settings). https://www.fastcompany.com/91120456/deepmind-alphafold-3-dna-rna-modeling https://www.fastcompany.com/91120456/deepmind-alphafold-3-dn...

bluerooibos 2y ago
That's pretty good. Based on the previous performance improvements of Alpha-- models, it'll be nearing 100% in the next couple of years.
- nybsjytm 2y ago
  Just "Alpha-- models" in general?? That's not a remotely reasonable way to reason about it. Even if it were, why should it stop DeepMind from clearly communicating accuracy?
  - dekhn 2y ago
    The way I think about this (specifically, deepmind not publishing their code or sharing their exact experimental results): advanced science is a game played by the most sophisticated actors in the world. Demis is one of those actors, and he plays the games those actors play better than anybody else I've ever seen. Those actors don't care much about the details of any specific system's accuracy: they care to know that it's possible to do this, and some general numbers about how well it works, and some hints what approaches they should take. And Nature, like other top journals, is more than willing to publish articles like this because they know it stimulates the most competitive players to bring their best games. (I'm not defending this approach, just making an observation)
    - nybsjytm 2y ago
      I think it's important to qualify that the relevant "game" is not advanced science per se; the game is business whose product is science. The aim isn't to do novel science; it's to do something which can be advertised as novel science. That isn't to cast aspersions on the personal motivations of Hassabis or any other individual researcher working there (which itself isn't to remove their responsibilities to public understanding); it's to cast aspersions on the structure that they're part of. And it's not to say that they can't produce novel or important science as part of their work there. And it's also not to say that the same tension isn't often present in the science world - but I think it's present to an extreme degree at DeepMind. (Sometimes the distinction between novel science and advertisably novel science is very important, as seems to be the case in the "new materials" research dopylitty linked to in these comments: here https://www.404media.co/google-says-it-discovered-millions-of-new-materials-with-ai-human-researchers/ https://www.404media.co/google-says-it-discovered-millions-o...)

deleted 2y ago

[deleted]

j7ake 2y ago

So it’s okay now to publish a computational paper with no code? I guess Nature’s reporting standards don’t apply to everyone. > A condition of publication in a Nature Portfolio journal is that authors are required to make materials, data, code, and associated protocols promptly available to readers without undue qualifications. > Authors must make available upon request, to editors and reviewers, any previously unreported custom computer code or algorithm used to generate results that are reported in the paper and central to its main claims. https://www.nature.com/nature-portfolio/editorial-policies/reporting-standards#availability-of-computer-code https://www.nature.com/nature-portfolio/editorial-policies/r...

boxed 2y ago
Are you an editor or reviewer?
- HanClinto 2y ago
  Good question. Also makes me wonder -- where's the line? Is it reasonable to have "layperson" reviewers? Is it reasonable to think that regular citizens could review such content?
  - Kalium 2y ago
    I think you will find that for the vast, vast majority of scientific papers there is significant negative expected value to even attempting to have layperson reviewers. Bear in mind that we're talking about papers written by experts in a specific field aimed at highly technical communication with other people who are experts in the same field. As a result, the only people who can usefully review the materials are drawn from those who are also experts in the same field. For an instructive example, look up the seminal paper on the structure of DNA: https://www.mskcc.org/teaser/1953-nature-papers-watson-crick-wilkins-franklin.pdf https://www.mskcc.org/teaser/1953-nature-papers-watson-crick... Ask yourself how useful comments from someone who did not know what an X-ray is, never mind anything about organic chemistry, would be in improving the quality of research or quality of communication between experts in both fields.
  - 2y ago

ein0p 2y ago

I’m inclined to ignore such pr fluff until they actually demonstrate a _practical_ result. Eg. cure some form of cancer or some autoimmune disease. All this “prediction of structure” has been in the news for years, and it seems to have resulted in nothing practically usable IRL as far as I can tell. I could be wrong of course, I do not work in this field

dekhn 2y ago
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.
- ein0p 2y ago
  Sure. If/when we see anything practical, that’ll be the right moment to pay attention. This is much like “quantum computing” where everyone who doesn’t know what it is is excited for some reason, and those that do know can’t even articulate any practical applications
  - dekhn 2y ago
    Feynman already articulated the one practical application for quantum computing: using it to simulate complex systems (https://www.optica-opn.org/home/articles/on/volume_11/issue_2/features/quantum_mechanical_computers/ https://www.optica-opn.org/home/articles/on/volume_11/issue_... and https://calteches.library.caltech.edu/1976/ https://calteches.library.caltech.edu/1976/ and https://s2.smu.edu/~mitch/class/5395/papers/feynman-quantum-1981.pdf https://s2.smu.edu/~mitch/class/5395/papers/feynman-quantum-... These approaches are now being explored but I haven't seen any smoking guns showing a QC-based simulation exceeding the accuracy of a classical computer for a reasonable investment. Folks have suggested other areas, such as logistics, where finding small improvements to the best approximations might give a company a small edge, and crypto-breaking, but there has been not that much progress in this area, and the approximate methods have been improving rapidly.

mchinen 2y ago

I am trying to understand how accurate the docking predictions are. Looking at the PoseBusters paper [1] they mention, they say they are 50% more accurate than traditional methods. DiffDock, which is the best DL based systems gets 30-70% depending on the dataset, and traditional gets 50-70%. The paper highlighted some issues with the DL-based methods and given that DeepMind would have had time to incorporate this into their work and develop with the PoseBusters paper in mind, I'd hope it's significantly better than 50-70%. They say 50% better than traditional so I expected something like 70-85% across all datasets. I hope a paper will appear soon to illuminate these and other details. [1] https://pubs.rsc.org/en/content/articlehtml/2024/sc/d3sc04185a https://pubs.rsc.org/en/content/articlehtml/2024/sc/d3sc0418...

_obviously 2y ago

[flagged]

dsign 2y ago

For a couple of years I've been expecting that ML models would be able to 'accelerate' bio-molecular simulations, using physics-based simulations as ground truth. But this seems to be a step beyond that.

dekhn 2y ago
When I competed in CASP 20 years ago (and lost terribly) I predicted that the next step to improve predictions would be to develop empirically fitted force fields to make MD produce accurate structure predictions (MD already uses empirically fitted force fields, but they are not great). This area was explored, there are now better force fields, but that didn't really push protein structure prediction forward. Another approach is fully differentiable force fields- the idea that the force field function itself is a trainable structure (rather than just the parameters/weights/constants) that can be optimized directly towards a goal. Also explored, produced some interesting results, but nothing that woudl be considered transformative. The field still generally believes that if you had a perfect force field and infinite computing time, you could directly recapitulate the trajectories of proteins folding (from fully unfolded to final state along with all the intermediates), but that doesn't address any practical problems, and is massively wasteful of resources compared to using ML models that exploit evolutionary information encoded in sequence and structures. In retrospect I'm pretty relieved I was wrong, as the new methods are more effective with far fewer resources.

xnx 2y ago

Very cool that anyone can login to https://golgi.sandbox.google.com/ https://golgi.sandbox.google.com/ and check it out

_akhe 2y ago

Google's Game of Life 3D: Spiral edition

uptownfunk 2y ago

Very sad to see they did not make it open source. When you have a technology that has the potential to be a gateway for drug development, to the cures of new diseases, and instead you choose to make it closed, it is a very huge disservice to the community at large. Sure, release your own product alongside it, but making it closed source does not help the scientific community upon which all these innovations were built. Especially if you have lost a loved one to a disease which this technology will one day be able to create cures for, it is very disappointing.

falcor84 2y ago
The closer it gets to enabling full drug discovery, the closer it also gets to enabling bioterrorism. Taking it to the extreme, if they had the theory of everything, I don't think I'd want it to be made available to the whole world as it is today. On a related note, I highly recommend The Talos Principle 2, which really made me think about these questions.
- pythonguython 2y ago
  Any organization/country that has the ability to use a tool like this to create a bio weapon is already sophisticated enough to do bioterrorism today.
  - ramon156 2y ago
    Alright, but now picture this: it's now open to the masses, meaning an individual could probably even do it.
    - uptownfunk 2y ago
      The risks don't exceed what is already out there. If someone wants to do damage, especially in America, there are more than enough ways they can do it already. The technology should be made free. I also wonder how much the claims are being exaggerated and are marketing-speak vs. real results. Is there any benchmark for this that they have published?

nojvek 2y ago

So much hyperbole from recent Google releases. I wish they didn't hype AI so much, but I guess that's what people want to hear, so they say that.

sangnoir 2y ago
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.

LarsDu88 2y ago

As a software engineer, I kind of feel uncomfortable about this new model. It outperforms Alphafold 2 at ligand binding, but Alphafold 2 also had some more hardcoded and interpretable structural reasoning baked into the model architecture. There's so many things you can incorporate into a protein folding model such as structural constraints, rotational equivariance, etc, etc This new model simple does away with some of that, achieving greater results. And the authors simply use distillation from data outputted from Alphafold2 and Alphafold2-multimer to get those better results for those cases where you wind up with implausible results. You have to run all those previous models, and output their predictions to do the distillation to achieve a real end-to-end training from scratch for this new model! Makes me feel a bit uncomfortable.

amitport 2y ago
Consider that humans also learn from other humans, and sometimes surpass their teachers. A bit more comfortable?
- Balgair 2y ago
  Ahh, but the new young master is able to explain their work and processes to the satisfaction of the old masters. In the 'Science' of our modern times it's a requirement to show your work (yes, yes, I know about the replication crisis and all that terrible jazz). Not being able to ascertain how and why the ML/AI is achieving results is not quite the same and more akin to the alchemists and sorcerers with their cyphers and hidden laboratories.
  - falcor84 2y ago
    > the new young master is able to explain their work and processes to the satisfaction of the old masters Yes, but it's one level deep - in general they wouldn't be able to explain their work to their master's master (note "science advances one funeral at a time").
  - hackerdood 2y ago
    I’ll add that specially when it comes to playing go, professionals who are at the peak of their ability can often find the best move at a given point but be unable to explain why beyond “it feels right” or “it looks right”.

roody15 2y ago

I wonder in the not too distant future if these AI predictions could be explained back into “humanized” understanding. Much like ChatGPT can simplify complex topics … cold the model in the future provide feedback to researchers why it is making this prediction?

deleted 2y ago
[deleted]

reliablereason 2y ago

Would be very useful if one they used it to predict the structure and interaction of the known variants to. Would be very helpful when predicting if a mutation on a protein would lead to loss of function for the protein.

mfld 2y ago

The improvement on predicting protein/RNA/ligand interactions might facilitate many commercially relevant use cases. I assume pharma and biotech will eagerly get in line to use this.

tonyabracadabra 2y ago

Very cool, and what’s cooler is this rap about alphafold3 https://heymusic.ai/blog/news/alphafold-3 https://heymusic.ai/blog/news/alphafold-3

wuj 2y ago

This tool reminds me that the human body functions much like a black box. While physics can be modeled with equations and constraints, biology is inherently probabilistic and unpredictable. We verify the efficacy of a medicine by observing its outcomes: the medicine is the input, and the changes in symptoms are the output. However, we cannot model what happens in between, as we cannot definitively prove that the medicine affects only its intended targets. In many ways, much of what we understand about medicine is based on observing these black-box processes, and this tool helps to model that complexity.

bamboozled 2y ago
I’d say it’s always been the case for medicine, when people first used medicines, the intention was never to fully understand what happens, just save a life, eliminate or reduce symptoms. Now we’ve built explainable systems like computers and software, we try to overlay that onto everything and it might not work. To quote Alan Watts, humans like to try square out wiggly systems because we’re not great and understanding wiggles.
a_bonobo 2y ago
Classic essay in this vein: >Can a biologist fix a radio? — Or, what I learned while studying apoptosis https://www.cell.com/cancer-cell/pdf/S1535-6108(02)00133-2.pdf https://www.cell.com/cancer-cell/pdf/S1535-6108(02)00133-2.p... >However, if the radio has tunable components, such as those found in my old radio (indicated by yellow arrows in Figure 2, inset) and in all live cells and organisms, the outcome will not be so promising. Indeed, the radio may not work because several components are not tuned properly, which is not reflected in their appearance or their connections. What is the probability that this radio will be fixed by our biologists? I might be overly pessimistic, but a textbook example of the monkey that can, in principle, type a Burns poem comes to mind. In other words, the radio will not play music unless that lucky chance meets a prepared mind.

lysozyme 2y ago

Probably worth mentioning that David Baker’s lab released a similar model (predicts protein structure along with bound DNA and ligands), just a couple of months ago, and it is open source [1]. It’s also worth remembering that it was David Baker who originally came up with the idea of extending AlphaFold from predicting just proteins to predicting ligands as well [2]. 1. https://github.com/baker-laboratory/RoseTTAFold-All-Atom https://github.com/baker-laboratory/RoseTTAFold-All-Atom 2. https://alexcarlin.bearblog.dev/generalized/ https://alexcarlin.bearblog.dev/generalized/ Unlike AlphaFold 3, which predicts only a small, preselected subset of ligands, RosettaFold All Atom predicts a much wider range of small molecules. While I am certain that neither network is up to the task of designing an enzyme, these are exciting steps. One of the more exciting aspects of the RosettaFold paper is that they train the model for predicting structures, but then also use the structure predicting model as the denoising model in a diffusion process, enabling them to actually design new functional proteins. Presumably, DeepMind is working on this problem as well.

theGnuMe 2y ago
And that tech just got $1b in funding.
- LeanderK 2y ago
  can you expand? Who got 1bn of funding?
refulgentis 2y ago
I appreciated this, but it's probably worth mentioning: when you say AlphaFold 3, you're talking about AlphaFold 2. TFA announces AlphaFold 3. Post: "Unlike AlphaFold 3, which predicts only a small, preselected subset of ligands, RosettaFold All Atom predicts a much wider range of small molecules" TFA: "AlphaFold 3...*models large biomolecules such as proteins, DNA and RNA*, as well as small molecules, also known as ligands" Post: "they also use the structure predicting model as the denoising model in a diffusion process...Presumably, DeepMind is working on this problem as well." TFA: "AlphaFold 3 assembles its predictions using a diffusion network, akin to those found in AI image generators."

bbstats 2y ago

Zero-shot nearly beating trained catboost is pretty amazing.

thenerdhead 2y ago

A lot of accelerated article previews as of recently. Seems like humanity is making a lot of breakthroughs. This is nothing short of amazing for all those suffering from disease.

ak_111 2y ago

If you work in this space would be interested to know what material impact has alphafold caused in your workflow since its release 4 years ago?

lumb63 2y ago

Would anyone more familiar with the field be able to provide some cursory resources on the protein folding problem? I have a background in computer science and a half a background in biology (took two semesters of OChem, biology, anatomy; didn’t go much further).

MPSimmons 2y ago

Not sure why the first thing they point it at wouldn't be prions.

itissid 2y ago

Noob here. Can one make the following deduction: In transformer based architectures, where one typically uses variation of attention mechanism to model interactions, even if one does not consider the autoregressive assumption of the domain's "nodes"(amino acids, words, image patches), if the number of final states that nodes take eventually can be permuted only in a finite way(i.e. they have sparse interactions between them), then these architectures are efficient way of modeling such domains. In plain english the final state of words in a sentence and amino acids in a protein have only so many ways they can be arranged and transformers do a good job of modeling it. Also can one assume this won't do well for domains where there is, say, sensitivity to initial conditions, like chaotic systems like wheather where the # final states just explodes?

ricksunny 2y ago

I'm interested in how they measure accuracy of binding site identification and binding pose prediction. This was missing for the hitherto widely-used binding pose prediction tool Autodock Vina (and in silico binding pose tools in general). Despite the time I invested in learning & exercising that tool, I avoided using it for published research because I could not credibly cite its general-use accuracy. Is / will Alphafold 3 be citeable in the sense of "I have run Alphafold on this particular target of interest and this array of ligands, and have found these poses of X kJ/mol binding energy, and this is known to an accuracy of Y% because of Alphafold 3's training set results cited below'

l33tman 2y ago
I've never trusted those predicted binding energies. If you have predicted a ligand/protein complex and have high confidence in it and want to study the binding energy I really think you should do a full MD simulation, you can pull the ligand-protein complex apart and measure the change in free energy explicitly. Also, and this is an unfounded guess only, the problem of protein / ligand docking is quite a bit more complex than protein folding - there seems to be a finite set of overall folds used in nature, while docking a small ligand to a big protein with flexible sidechains and even flexible large-scale structures can have induced fits that are really important to know and estimate, and I'm just very sceptical that it's going to be possible to in a general fashion ever predict these accurately by the AI model with the limited training data. Though you just need some hints, then you can run MD sims on them to see what happens for real.

TaupeRanger 2y ago

So after 6 years of this "revolutionary technology", what we have to show for all the hype and breathless press releases is: ....another press release saying how "revolutionary" it is. Fantastic. Thanks DeepMind.

dev1ycan 2y ago

Excited but also it's been a fair bit now and I have yet to see something truly remarkable come out of this

zmmmmm 2y ago

So much of the talk about their "free server" seems to be trying to distract from the fact that they are not releasing the model. I feel like it's an important threshold moment if this gets accepted into scientific use without the model being available - reproducibility of results becomes dependent on the good graces of a single commercial entity. I kind of hope that like OpenAI it just spurs creation of equivalent open models that then actually get used.

nsoonhui 2y ago

Here's something that bugs me about ML: all we have is prediction and no explanation how we come to that prediction, ie: no deeper understanding on the underlying principles. So despite that we got a good match this time, how can we be sure that the match will be equally good next time? And how to use ML to predict structure that we have no baseline to start with or experimental result to benchmark ? In the absence of physics-like principles, How can we ever be sure that ML results next time is correct ?

throwaway4aday 2y ago
Speaking of physics, we should borrow the quote "Shut up and calculate" to describe the situation: it works so use it now and worry about the explanations later.
- d0mine 2y ago
  Except the model is not open-source. You can't calculate anything.
coriny 2y ago
There is a biannual structural prediction contest called CASP [1], in which a set of newly determined structures is used to benchmark the prediction methods. Some of these structures will be "novel", and so can be used to estimate the performance of current methods on predicting "structure that we have no baseline to start with". CASP-style assessments are something that should done for more research fields, but it's really hard to persuade funders and researchers to put up the money and embargo the data as required. [1] https://en.wikipedia.org/wiki/CASP https://en.wikipedia.org/wiki/CASP

sidcool 2y ago

And google is giving the service for free. Pretty good.

gajnadsgjoas 2y ago

Can someone tell me what are the direct implication of this? I often see "helps with a drug design" but I'm too far from this industry and have never seen an example of such drugs

Syzygies 2y ago
We're this much closer to being hacked?

DF1PAW 2y ago

Does it also simulate prion (=misfolded structures) based diseases?

niwaniwaaa 2y ago

How can I get my outputs in PDB format? Can not?

deleted 2y ago

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