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

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

#171

Post by RobCat27 »

mberning wrote: post #113
moconnor wrote: post #32

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-int…

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

I believe this is the case also. With a well enough performing AI/ML/probabilistic model where you can change the model's input parameters and get a highly accurate prediction basically instantly, we can test theories approximately and extremely fast rather than running completely new experiments, which will always come with it's own set of errors and problems.

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

#172

Post by nybsjytm »

dekhn wrote: post #141
nybsjytm wrote: post #114

Earlier quoted context omitted.

I don't know Paul Adams, so it's hard for me to know how to interpret your post. Is there anything else I can read that discusses the accuracy of AlphaFold?

Yes, https://predictioncenter.org/casp15/ https://www.sciencedirect.com/science/article/pii/S0959440X2... https://dasher.wustl.edu/bio5357/readings/oxford-alphafold2.... I can't find the link at the moment but from the perspective of the CASP leaders, AF2 was accurate enough that it's hard to even compare to the best structures determined experimentally, due to noise in the data/inadequacy of the metric. A number of…

Thanks, those look helpful. Whenever I meet someone with relevant PhDs I ask their thoughts on AlphaFold, and I've gotten a wide variety of responses, from responses like yours to people who acknowledge its usefulness but are rather dismissive about its ultimate contribution.

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

#173

Post by ein0p »

dekhn wrote: post #110
ein0p wrote: post #81

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

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 computa…

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

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

#174

Post by dekhn »

interroboink wrote: post #167
dekhn wrote: post #93

Earlier quoted context omitted.

If you're a scientist who works in protein folding (or one of those other areas) and strongly believe that science's goal is to produce falsifiable hypotheses, these new approaches will be extremely depressing, especially if you aren't proficient enough with ML to reproduce this work in your own hands. If you're a scientist who accepts that probabilist models beat interpretable ones (articulated well here: https://no…

> ... and strongly believe that science's goal is to produce falsifiable hypotheses, these new approaches will be extremely depressing I don't quite understand this point — could you elaborate? My understanding is that the ML model produces a hypothesis, which can then be tested via normal scientific method (perform experiment, observe results). If we have a magic oracle that says "try this, it will work", and then w…

People will be depressed because they spent decades getting into professorship positions and publishing papers with ostensible comprehensible interpretations of the generative processes that produced their observations, only to be "beat" in the game by a system that processed a lot of observations and can make predicts in a way that no individual human could comprehend. And those professors will have a harder time publishing, and therefore getting promoted, in the future.

Whether ML models produce hypotheses is something of an epistemiological argument that I think muddies the waters without bringing any light. I would only use the term "ML models generate predictions". In a sense, the model itself is the hypothesis, not any individual prediction.

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

#175

Post by topaz0 »

moconnor wrote: post #32

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-int…

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

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

#176

Post by uptownfunk »

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.

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

#177

Post by bluerooibos »

moconnor wrote: post #32

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-int…

> What happens when...

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

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

#178

Post by GistNoesis »

moconnor wrote: post #32

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-int…

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

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

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

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

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

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

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

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

#179

Post by visarga »

moconnor wrote: post #32

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-int…

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

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

#180

Post by bluerooibos »

nybsjytm wrote: post #62

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-dn...

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