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SimpleFold: Folding proteins is simpler than you think
https://arxiv.org/abs/2509.18480 https://arxiv.org/abs/2509.18480
- kylehotchkiss 1y ago> Folding Proteins Is Simpler Than You Think Then why do we need customized LLM models, two of which seemed to require the resources of 2 of the wealthiest companies on earth (this and google's alphafold) to do it?
- wrs 1y agoHow simple did you think it was before?
- kylehotchkiss 1y agoNot simple! Wasn't/Isn't X-ray crystallography what it usually takes to determine the structure?
- wrs 1y agoYes, that’s the empirical observation method and the source of ground truth for model training.
- wrsh07 1y agoFolding proteins is pretty valuable and this model is comparably small This doesn't seem like particularly wasteful overinvestment. Granted, I'm more excited about the research coming out of arc
- jjtheblunt 1y agowhat are you referring to by arc?
- aDyslecticCrow 1y agoIts not an LLM, It's a transformer. I know the terms are really being butchered in media, but if we're gonna use the term LLM instead of AI, we better make sure it's actually a "large language model" that is being refereed to. If you're unsure, call it a neural net, or machine learning algorithm, or AI. It's indeed a large model. But if you knew the history of the field, it's a massive improvement. It has progressed from a almost "NP" problem only barely approachable with distributed cluster compute, to something that can run on a single server with some pricey hardware. The smallest model is only here is only 100M parameters and the largest is 3B parameters, that's very approachable to run locally with the right hardware, and easily within the range for a small biotech lab (compared to the cost of other biotech equipment) It's also (i'd argue) one of the only truly economically and sociably valuable AI technologies we've found over the past few years. Every simulated protein fold is saving a biotech company weeks of work for highly skilled biotech engineers and very expensive chemicals (In a way that that truly only supplement rather than replace the work). Any progress in the field is a huge win for society.
- kylehotchkiss 1y agoI'm more teasing the title than the tech :) I'm all for innovation in the field especially with so much bio funding cut!
- barbarr 1y agoWhy is apple doing protein folding?
- mabedan 1y agoProwlly cuz Siri didn’t work out
- Forbo 1y agoReputation laundering?
- jama211 1y agoWhat’s there to launder? Perhaps they shouldn’t have as good a reputation as they do, but you can’t deny they do have a good reputation.
- amelius 1y agoReputation of what? They are just an office appliance company.
- axoltl 1y agoYou're confusing your opinion of the company with the perception by the general public. Apple's definitely not perceived as 'an office appliance company' by your average person. It's considered a high-end luxury brand by many[1]. 1: https://www.researchgate.net/publication/361238549_Consumer_perception_of_high-tech_brands_and_related_products_The_case_of_the_iconic_Apple https://www.researchgate.net/publication/361238549_Consumer_...
- commandersaki 1y agoI think you mean high-tech brand, which the linked article affirms.
- robotresearcher 1y ago
- wild_pointer 1y agoDid you just assume what I think about protein folding simplicity?!
- IAmBroom 1y agoLink goes the github repository behind the article you might want to read. https://arxiv.org/abs/2509.18480 https://arxiv.org/abs/2509.18480
- IAmBroom 1y agoAnd the abstract alone says (if I'm reading it correctly), "It still takes AI; just not nearly as much as others are doing."
- mentalgear 1y agoanother form: transformers for the task
- serjester 1y agoFor those interested in the GitHub link. https://github.com/apple/ml-simplefold https://github.com/apple/ml-simplefold
- turblety 1y agoI wonder why Apple can create a model to fold proteins, but still can't get Siri to control the phone competently? I'm not sure I agree with Apple's priorities. I guess these things are not synchronous and they can work on multiple things at a time.
- tanelpoder 1y agoI guess it's because SimpleFold came from a research lab with different autonomy and less competing interests and internal politics...
- frenchie4111 1y agoI am genuinely interested where the strong negativity towards Siri has come from in recent culture. From what I gather it's likely due to the high expectations we have for Apple. But what I don't really get is why is there not a similar amount of negativity being directed at Google or Samsung, who both have equally shit phone AI assistants (obviously this is just from my perspective, I am a daily user of both iOS and a Samsung Android) I am not trying to defend Apple or Siri by any means. I think the product absolutely should (and will) improve. I am just curious to explore why there is such negativity being directed specifically at Apple's AI assistant.
- Invictus0 1y agoFor the last three iOS major versions, Siri has been unable to execute the simple command "shuffle the playlist 'Jams'", or any variation, like "play the playlist Jams on shuffle". I am upset for that reason.
- deleted 1y ago[deleted]
- samuelg123 1y agoI think Siri has always been criticized, likely because it has never worked super well and it has the most eyes (or ears) on it (iPhones still have 50% market share in the US). And now that we have ChatGPT with voice mode, Gemini Live, etc which have incredible speech recognition and reasoning comparatively, it's harder to argue that "every voice assistant is bad" still.
- stephenpontes 1y agoI remember first hearing about protein folding with the Folding @Home project (https://foldingathome.org https://foldingathome.org) back when I had a spare media server and energy was cheap (free) in my college dorm. I'm not knowledgable on this, but have we come a long way in terms of making protein folding simpler on today's hardware, or is this only applicable to certain types of problems? It seems like the Folding @Home project is still around!
- nkjoep 1y agoTeam F@H forever!
- _joel 1y agoYep, that and SETI@Home. I loved the eye candy, even if I didn't know what it fully meant.
- seydor 1y agoHow come we don't have AI@Home
- throwup238 1y agoThe network bandwidth between nodes is a bigger limitation than compute. The newest Nvidia cards come with 400gbit busses now to communicate between them, even on a single motherboard. Compared to SETI or Folding @Home, this would work glacially slow for AI models.
- fourthark 1y agoSeems like training would be a better match, where you need tons of compute but don’t care about latency.
- ronsor 1y agoNo, the problem is that with training, you do care about latency, and you need a crap-ton of bandwidth too! Think of the all_gather; think of the gradients! Inference is actually easier to distribute.
- foodevl 1y agoI was curious what the protein picture was showing: "Figure 1 Example predictions of SimpleFold on targets ... with ground truth shown in light aqua and prediction in deep teal." and now I'm even more curious why they thought "light aqua" vs "deep teal" would be a good choice
- gilleain 1y agoWell, figure a) shows a ribbon representation of the fold (as helices and strands) of the protein 7QSW (https://www.ebi.ac.uk/pdbe/entry/pdb/7qsw https://www.ebi.ac.uk/pdbe/entry/pdb/7qsw) which is RubisCO (https://en.wikipedia.org/wiki/RuBisCO https://en.wikipedia.org/wiki/RuBisCO), an plant protein that plays a key role in photosynthesis. The different colours are for the predicted and 'real' (ground truth) models. The fact that it is hard to distinguish is partly the - as you point out - weird colour choice, but also because they are so close together. An inaccurate prediction would have parts that stand out more as they would not align well in 3D space.
- underdeserver 1y agoSo, how does this compare to AlphaFold?
- mentalgear 1y agoseems like they use the normal transformer architecture versus deep fold's more specialised machine-learning approaches.
- Invictus0 1y agoThey'll do anything but fix Siri
- mentalgear 1y agoThey can keep on doing stuff like this that's open-source and beneficial to society.
- frenchie4111 1y agoI am curious to hear an expert weigh in on this approach's implications for protein folding research. This sounds cool but it's really unclear to me what the implications are
- geremiiah 1y agoTheir representation is simpler, just a transformer. That means you can just plug in all the theory and tools that have been developed specifically for transformers, most importantly you can scale the model easier. But more than that, I think, it shows that there was no magic to AlphaFold. The details of the architecture and training method didn't matter much. All that was needed was training a big enough model on a large enough dataset. Indeed lots of people who have experimented with AlphaFold have found it to behave similiar to LLMs, i.e. it performs well on inputs close to the training dataset and but it doesn't generalize well at all.
- visarga 1y ago> But more than that, I think, it shows that there was no magic to AlphaFold. The details of the architecture and training method didn't matter much. All that was needed was training a big enough model on a large enough dataset. People often like to say that we just need one more algorithmic breakthrough or two for AGI. But in reality it's the dataset and the environment based learning. Almost any model would do if you collected the data. It's not in the model, it's outside where we need to work on.
- aDyslecticCrow 1y agoI think the sentiment that simplicity is good, is a false conclusion. Simplicity is simply good scientific methodology. Doing too many things at once makes methods hard to adopt and makes conclusions harder to draw. So we try to find simple methods that show measurable gain, so we can adapt it to future approaches. Its a cycle between complexity and simplicity. When a new simple and scalable approach beats the previous state of art, that just means we discovered a new local maxima hill to climp up.
- johncolanduoni 1y ago
- 331c8c71 1y agoIt is for structure prediction, not folding (rolleyes).
- jandom 1y agoPssst they'll realise scientists hand out here too
- barbazoo 1y agoNo folding here. Proteins go on the hanger or in the drawer.
- kazinator 1y agoI'm satisfied with with folding roast beef onto a sandwich, or folding egg whites into batter. All the protein folding action I could ever want.
- vbarrielle 1y agoA paper that says: "our approach is simpler than the state of the art". But also does not loudly say "our approach is significantly behind the state of the art on all metrics". Not easy to get published, but I guess putting it as a preprint with a big company's name will help...
- shpongled 1y agoIt's not totally novel, but it's very cool to see the continued simplification of protein folding models - AF2 -> AF3 was a reduction in model architecture complexity, and this is a another step in the direction of the bitter lesson.
- hashta 1y agoI’m not sure AF3’s performance would hold up if it hadn’t been trained on data from AF2 which itself bakes in a lot of inductive bias like equivariance
- nextworddev 1y agoIn industry Google practically dominates this field
- hashta 1y agoOne caveat that’s easy to miss: the "simple" model here didn’t just learn folding from raw experimental structures. Most of its training data comes from AlphaFold-style predictions. Millions of protein structures that were themselves generated by big MSA-based and highly engineered models. It’s not like we can throw away all the inductive biases and MSA machinery, someone upstream still had to build and run those models to create the training corpus.
- mapmeld 1y agoAnd AlphaFold was validated with experimental observation of folded proteins using X-rays
- godelski 1y agoIs this so unusual? Almost everything that is simple was once considered complex. That's the thing about emergence, you have to go through all the complexities first to find the generalized and simpler formulations. It should be obvious that things in nature run off of relatively simple rulesets, but it's like looking at a Game of Life and trying to reverse engineer those rules AND the starting parameters. Anyone telling you such a task is easy is full of themselves. But then again, who seriously believes that P=NP?
- hashta 1y agoTo people outside the field, the title/abstract can make it sound like folding is just inherently simple now, but this model wouldn’t exist without the large synthetic dataset produced by the more complex AF. The "simple" architecture is still using the complex model indirectly through distillation. We didn’t really extract new tricks to design a simpler model from scratch, we shifted the complexity from the model space into the data space (think GPT-5 => GPT-5-mini, there’s no GPT-5-mini without GPT-5)
- stavros 1y agoBut this is just a detail, right? If we went and painstakingly catalogued millions of proteins, we'd be able to use the simple model without needing a complex model to generated data, no?
- dyauspitr 1y agoIsn’t this a largely solved problem after Alphafold?
- samfriedman 1y agoMaybe they've been working on it, but got scooped?
- zamadatix 1y agoI don't think that's the case. The numbers in the paper suggest ~92% of the training data comes from pre-existing AI models, including AlphaFold, and they claim things like: > We largely adopt the data pipeline implemented in Boltz-11 1https://github.com/jwohlwend/boltz https://github.com/jwohlwend/boltz (Wohlwend et al., 2024), which is an open-source replication of AlphaFold3 I believe the story here is largely that they simplified the architecture and scaled it to 3B parameters while maintaining leading results.
- the__alchemist 1y agoShould an entry in a field preclude other ones. I encourage you to apply reductio-ad-absurdum here. Should Pepsi exist if Coke does? Should C exist if Fortran does?
- phoenicyan 1y agoCurious since AlphaFold got released: have classical molecular dynamics sims in this area become obsolete, at least for protein folding? How does the research coming out of venues like DESRES compare? Are they working on more specific problems in the same area or are they in a different business altogether?
- the__alchemist 1y agoNo. AlphaFold doesn't do dynamics; it does end-state snapshots only. It does not do anything about the motion of the atoms, which is the core functionality of MD.
- tripplyons 1y agoI was curious about what was released, and the parameters for AlphaFold V3 are only given to certain groups for non-commercial use: https://github.com/google-deepmind/alphafold3?tab=readme-ov-file#obtaining-model-parameters https://github.com/google-deepmind/alphafold3?tab=readme-ov-... However, it seems like anyone can download the parameters for AlphaFold V2: https://github.com/google-deepmind/alphafold?tab=readme-ov-file#model-parameters https://github.com/google-deepmind/alphafold?tab=readme-ov-f...
- dekhn 1y agoMD was never really a viable way to do structure prediction, so it didn't become obsolete with AlphaFold. Instead, MD is more useful for studying the physical process of protein folding (before the protein folds to its final structure, as well as once it has reached its final structure and sort of jiggles and wiggles around that).
- cowsandmilk 1y agoMD simulations typically aren’t run for time scales that tell you anything about the folding process. Most people are looking at motion after the protein has folded.
- temptemptemp111 1y ago[dead]
- ziofill 1y agoIn the plots in Fig. 4 it looks like they should have continued the training because the performance was still climbing, am I reading it incorrectly?
- GistNoesis 1y agoIntellectually, I don't like this approach. Predicting the end-result from the sequence of protein directly is prone to miss any new phenomenon and would just regurgitate/interpolate the training datasets. I would much prefer an approach based on first principles. In theory folding is easy, it's just running a simulation of your protein surrounded by some water molecules for the same number of nano-seconds nature do. The problem is that usually this take a long time because evolving a system needs to compute the energy of the system as a position of the atoms which is a complex problem involving Quantum Mechanics. It's mostly due to the behavior of the electrons, but because they are much lighter they operate on a faster timescale. You typically don't care about them, only the effect they have on your atoms. In the past, you would use various Lennard-Jones potentials for pairs of atoms when the pair of atoms are unbounded, and other potentials when they are bonded and it would get very complex very quickly. But now there are deep-learning based approach to compute the energy of the system by using a neural network. (See (Gromacs) Neural Network Potentials https://rowansci.com/publications/introduction-to-nnps https://rowansci.com/publications/introduction-to-nnps ). So you train these networks so that they learn the local interactions between atoms based on trajectories generated from ab-initio theories. This allows you to have a faster simulator which approximate the more complex physics. It's in a sort just tabulating using a neural network the effect of the electrons would have in a specific atom arrangements according to the theory you have chosen. At any time if you have some doubt, you can always run the slower simulator in the small local neighborhood to check that the effective field neural network approximation holds. Only then once you have your simulator which is able to fold, you can generate some dataset of pairs "sequence of protein" to "end of trajectory", to learn the shortcut like Alpha/Simple/Fold do. And when in doubt you can go back to the slower more precise method. If you had enough data and can train perfectly a model with sufficient representation power, you could theoretically infer the correct physics just from the correspondence initial to final arrangements. But if you don't have enough data it will just learn some shortcut and accept that it will be wrong some times.
- slashdave 1y ago> it's just running a simulation of your protein surrounded by some water molecules for the same number of nano-seconds nature do. No, the environment is important. Also, some proteins fold while being sequenced. Folding can also take minutes in some cases, which is the real problem. > which is a complex problem involving Quantum Mechanics Most MD simulations use classical approximations, and I don't see why folding is any different.
- tzumby 1y agoFlow-matching, the technique they describe is incredibly interesting. I studied it in the context of generative AI and found it fascinating. It’s so fitting that a technique that borrows from thermodynamics and uses Brownian motion would go full circle to solve for protein folding.
- alex77456 1y agoSemi related, Veritassium channel made a nice video on protein folding https://www.youtube.com/watch?v=P_fHJIYENdI https://www.youtube.com/watch?v=P_fHJIYENdI
- skepticaf 1y ago[dead]
- nicohayes 1y agoThis is a classic knowledge distillation pattern in ML - the "teacher" models (AlphaFold, ESMFold) with complex MSA-based architectures generate training data for a simpler "student" model. What's particularly interesting is how well the simplified architecture generalizes despite losing the evolutionary signal from MSAs. The performance suggests that much of the MSA complexity might be capturing patterns that can be learned more directly from structure data. This could be huge for real-time applications where MSA computation is the bottleneck. Has anyone benchmarked inference speed comparisons with the original AlphaFold pipeline?
- nicohayes 1y agoThis is a classic knowledge distillation pattern in ML - the "teacher" models (AlphaFold, ESMFold) with complex MSA-based architectures generate training data for a simpler "student" model. What s particularly interesting is how well the simplified architecture generalizes despite losing the evolutionary signal from MSAs. The performance suggests that much of the MSA complexity might be capturing patterns that can be learned more directly from structure data. This could be huge for real-time applications where MSA computation is the bottleneck. Has anyone benchmarked inference speed comparisons with the original AlphaFold pipeline?