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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

#181

Post by topaz0 »

mathgradthrow wrote: post #124
_yb2s wrote: post #120

Earlier quoted context omitted.

It means we now have an accurate surrogate model or "digital twin" that can be experimented on almost instantaneously. So we can massively accelerate the traditional process of developing mechanistic understanding through experiment, while also immediately be able to benefit from the ability to make accurate predictions, even without needing understanding. In reality, science has already pretty much gone this way lon…

instead of "in mice", we'll be able to say "in the cloud"

"In nimbo" (though what people actually say is "in silico").

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

#182

Post by dekhn »

natechols wrote: post #125
p3opl3 wrote: post #90

Earlier quoted context omitted.

Yes, because that's going to stop competitors.. it's why they didn't release code I guess. This is yet another large part of a biotech related Gutenberg moment.

The DeepMind team was essentially forced to publish and release an earlier iteration of AlphaFold after the Rosetta team effectively duplicated their work and published a paper about it in Science. Meanwhile, the Rosetta team just published a similar work about co-folding ligands and proteins in Science a few weeks ago. These are hardly the only teams working in this space - I would expect progress to be very fast in…

How much has changed- I talked with David Baker at CASP around 2003 and he said at the time, while Rosetta was the best modeller, every time they updated its models with newly determined structures, its predictions got worse :)

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

#183

Post by visarga »

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…

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.

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

#184

Post by visarga »

narrator wrote: post #140
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…

What if our understanding of the laws of the natural sciences are subtly flawed and AI just corrects perfectly for our flawed understanding without telling us what the error in our theory was? Forget trying to understand dark matter. Just use this model to correct for how the universe works. What is actually wrong with our current model and if dark matter exists or not or something else is causing things doesn't matt…

ML is accustomed with the idea that all models are bad, and there are ways to test how good or bad they are. It's all approximations and imperfect representations, but they can be good enough for some applications.

If you think carefully humans operate in the same regime. Our concepts are all like that - imperfect, approximative, glossing over some details. Our fundamental grounding and test is survival, an unforgiving filter, but lax enough to allow for anti-vaxxer movements during the pandemic - survival test is not testing for truth directly, only for ideas that fail to support life.

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

#185

Post by topaz0 »

insane_dreamer wrote: post #103
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…

For me the big question is how do we confidently validate the output of this/these model(s).

It's the right question to ask, and the answer is that we will still have to confirm them by experimental structure determination.

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

#186

Post by dekhn »

nybsjytm wrote: post #172
dekhn wrote: post #141

Earlier quoted context omitted.

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.

The people who are most likely to deprecate AlphaFold are the ones whose job viability is directly affected by its existence.

Let me be clear: DM only "solved" (and really didn't "solve") a subset of a much larger problem: creating a highly accurate model of the process by which real proteins adopt their folded conformations, or how some proteins don't adopt folded conformations without assistance, or how some proteins don't adopt a fully rigid conformation, or how some proteins can adopt different shapes in different conditions, or how enzymes achieve their catalyst abilities, or how structural proteins produce such rigid structures, or how to predict whether a specific drug is going to get FDA approval and then make billions of dollars.

In a sense we got really lucky because CASP has been running so long and with some many contributors that it became recognized that winning at CASP meant "solving protein structure prediction to the limits of our ability to evaluate predictions", and that Demis and his associates had such a huge drive to win competitions that they invested tremendous resources and state of the art technology, while sharing enough information that the community could reproduce the results in their own hands. Any problem we want solved, we should gamify, so that DeepMind is motivated to win the game.

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

#187

Post by slibhb »

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.

If anyone actually thought this way -- no one does -- they definitely wouldn't build models like this.

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

#188

Post by slibhb »

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…

It's interesting to compare this situation to earlier eras in science. Newton, for example, gave us equations that were very accurate but left us with no understanding at all of why they were accurate.

It seems like we're repeating that here, albeit with wildly different methods. We're getting better models but by giving up on the possibility of actually understanding things from first principles.

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

#190

Post by LarsDu88 »

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.

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