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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

#231

Earlier quoted context omitted.

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

Pure prediction is only all we need if the total end-to-end process is predicted correctly - otherwise there could be pretty nasty traps (e.g., drug works perfectly for the target disease but does something unexpected elsewhere etc.).

> e.g., drug works perfectly for the target disease but does something unexpected elsewhere etc.

That's very common. It's the reason to test the new drug in petri dish, then rats, then dogs, then humans and if all test passed send it to the pharmacy.

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

#232
post #93

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…

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…

So the work to simplify ML models, reduce dimensions, etc. becomes the numeric way to seek simple actual scientific models. Scientific computing and science become one.

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

#233
post #218

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...

IIRC the next best models all have all been using AlphaFold 2's methodology, so that's still a massive improvement. Edit: I see now that you're probably objecting to the headline that got edited on HN.

Not just the headline, the whole press release. And not questioning that it's a big improvement.

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

#234

Earlier quoted context omitted.

What makes this such a "deeply broken situation"? I agree that late-stage capitalism can create really tough situations for poor families trying to afford drugs. At the same time, I don't know any other incentive structure that would have brought us a breakthrough like AlphaFold this soon. For the first time in history, we have ML models that are beating out the scientific models by huge margins. The very fact that t…

Late-stage capitalism didn't bring us AlphaFold, scientists did, late-stage capitalism just brought us Alphabet swooping in at literally the last minute. Socialize the innovation because that requires potential losses, privatize the profits, basically. It's reminiscent of "Heroes of CRISPR," where Doudna and Charpentier are supposedly just some middle-men, because stepping in at the last minute with more funding is r…

Why haven't the academics created a non profit foundation with open source models like this then? If alphabet doesnt provide much, then they will be supplanted by non profits. I see nothing broken here.

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

#235

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…

Consider that humans also learn from other humans, and sometimes surpass their teachers. A bit more comfortable?

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.

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

#236
post #207

Earlier quoted context omitted.

There have been times in the past when usable technology surpassed our scientific understanding, and instead of being depressing it provided a map for scientific exploration. For example, the steam engine was developed by engineers in the 1600s/1700s (Savery, Newcomen, and others) but thermodynamics wasn’t developed by scientists until the 1800s (Carnot, Rankine, and others).

I think the various contributors to the invention of the steam engine had a good idea of what they were trying to do and how their idea would physically work. Wikipedia lists the prerequisites as the concepts of a vacuum and pressure, methods for creating a vacuum and generating steam, and the piston and cylinder.

That's not too different from the alpha fold people knowing that there's a sequence to sequence translation, that an enormous number of cross-talk happens between the parts of the molecule, that if you get the potential fields just right, it'll fold in the way nature intended. They're not just blindly fiddling with a bunch of levers. What they don't know is the individual detailed interactions going on and how to approximate them with analytical equations.

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

#237

Earlier quoted context omitted.

But the human brain has limited working memory and experience. Even in software development we are often teetering at the edge of the mental power to grasp and relate ideas. We have tried so much to manage complexity, but real world complexity doesn't care about human capabilities. So there might be high dimensional problems where we simply can't use our brains directly.

A human mind is perfectly capable of following the same instructions as the computer did. Computers are stupidly simple and completely deterministic. The concern is about "holding it all in your head", and depending on your preferred level of abstraction, "all" can perfectly reasonably be held in your head. For example: "This program generates the most likely outputs" makes perfect sense to me, even if I don't unders…

This is just wrong.

While computer operations in solutions are computable by humans, the billions of rapid computations are unachievable by humans. In just a few seconds, a computer can perform more basic arithmetic operations than a human could in a lifetime.

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

#238
post #194
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…

The goal of science has always been to discover underlying principles and not merely to predict the outcome of experiments. I don't see any way to classify an opaque ML model as a scientific artifact since by definition it can't reveal the underlying principles. Maybe one could claim the ML model itself is the scientist and everyone else is just feeding it data. I doubt human scientists would be comfortable with that…

Can underlying principles be discovered using the framework of scientific method? The primary goal of models and theories it develops is to support more experiments and eventually be disproven. If no model can be correct, complete and provable in finite time, then a theory about underlying principles that claims completeness would have to be unfalsifiable. This is reasonable in context of philosophy, but not in natural sciences.

Scientific method can help us rule out what underlying principles are definitely not. Any such principles are not actually up to be “discovered”.

If probabilistic ML comes along and does a decent job at predicting things, we should keep in mind that those predictions are made not in context of absolute truth, but in context of theories and models we have previously developed. I.e., it’s not just that it can predict how molecules interact, but that the entire concept of molecules is an artifact of just some model we (humans) came up with previously—a model which, per above, is probably incomplete/incorrect. (We could or should use this prediction to improve our model or come up with a better one, though.)

Even if a future ML product could be creative enough to actually come up with and iterate on models all on its own from first principles, it would not be able to give us the answer to the question of underlying principles for the above-mentioned reasons. It could merely suggest us another incomplete/incorrect model; to believe otherwise would be to ascribe it qualities more fit for religion than science.

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

#239
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.

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

#240

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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