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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

#241

Post by ThomPete »

dekhn wrote: post #93
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…

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 should always be to seek good explanations hard to vary.

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

#242

Post by EventH- »

croniev wrote: post #155
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…

I'm in the following camp: It is wrong to think about the world or the models as "complex systems" that may or may not be understood by human intelligence. There is no meaning beyond that which is created by humans. There is no 'truth' that we can grasp in parts but not entirely. Being unable to understand these complex systems means that we have framed them in such a way (f.e. millions of matrix operations) that doe…

"There is no 'truth' that we can grasp in parts but not entirely."

The value of pi is a simple counterexample.

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

#243

Post by interroboink »

variadix wrote: post #199
interroboink wrote: post #167

Earlier quoted context omitted.

> ... 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…

There is an issue scientifically. I think this point was expressed by Feynman: the goal of scientific theories isn’t just to make better predictions, it’s to inform us about how and why the world works. Many ancient civilizations could accurately predict the position of celestial bodies with calendars derived from observations of their period, but it wasn’t until Copernicus proposed the heliocentric model and Galileo…

I agree the how/why is the main driving goal. That's kinda why I feel like this is not depressiong news — there's a new frontier to discover and attempt to explain. Scientists love that stuff (:

Knowing how to predict the motion of planets but without having an underlying explanation encourages scientists to develop their theories. Now, once more, we know how to predict something (protein folding) but without an underlying explanation. Hurray, something to investigate!

(Aside: I realize that there are also more human factors at play, and upsetting the status quo will always cause some grief. I just wanted to provide a counterpoint that there is some exciting progress represented here, too).

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

#244

Post by RajT88 »

dekhn wrote: post #93
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…

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…

> can we actually interpret these models, or are human brains too feeble to understand complex systems without sophisticated models?

I think we will have to develop a methodology and supporting toolset to be able to derive the underlying patterns driving such ML models. It's just too much for a human to comb through by themselves and make sense of.

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

#245

Post by lysozyme »

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

2. 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.

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

#246

Post by ajuc »

jvanderbot wrote: post #210
visarga wrote: post #183

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…

Abstraction isn't the silver bullet. Not everything is abstractable.

"This program generates the most likely outputs" isn't a scientific explanation, it's teleology.

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

#248

Post by GuB-42 »

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…

We already have the absolute best method for accurately predicting the world, and it is by experimentation. In the protein folding case, it works by actually making the protein and analyzing it. For designing airplanes, computer models are no match for building the thing, or even using physical models and wind tunnels.

And despite having these "best method", it didn't prevent progress in theoretical physics, theory and experimentation complement each other.

ML models are just another kind of model that can help both engineering and fundamental research. Their working is close to the old guy in the shop who knows intuitively what is good design, because he has seen it all. That old guys in shops are sometimes better than modeling using physics equations help scientific progress, as scientists can work together with the old guy, combining the strength of intuition and experience with that of scientific reasoning.

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

#249

Post by jvanderbot »

GenerocUsername wrote: post #237
jvanderbot wrote: post #210

Earlier quoted context omitted.

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.

I'm not saying it's achievable, I'm saying it's not magic. A chemist who wishes to understand what the model is doing can get as far as anyone else, and can reach a level of "this prediction machine works well and I understand how to use and change it". Even if it requires another PhD in CS.

That the tools became complex is not a reason to fret in science. No more than statistical physics or quantum mechanics or CNN for image processing - it's complex and opaque and hard to explain but perfectly reproduceable. "It works better than my intuition" is a level of sophistication that most methods are probably doomed to achieve.

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