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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

#301

Post by pythonguython »

falcor84 wrote: post #277
uptownfunk wrote: post #176

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 buil…

The closer it gets to enabling full drug discovery, the closer it also gets to enabling bioterrorism. Taking it to the extreme, if they had the theory of everything, I don't think I'd want it to be made available to the whole world as it is today. On a related note, I highly recommend The Talos Principle 2, which really made me think about these questions.

Any organization/country that has the ability to use a tool like this to create a bio weapon is already sophisticated enough to do bioterrorism today.

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

#303

Post by dekhn »

panabee wrote: post #289
dekhn wrote: post #23

Earlier quoted context omitted.

AlphaFold very explicitly (unless something has changed) removes NMR structures as references because they are not accurate enough. I have a PhD in NMR biomolecular structure and I wouldn't trust. the structures for anything.

interesting observation and experience. must have made thesis development complex, assuming the realization dawned on you during the phd. what do you trust more than NMR? AF's dependence on MSAs also seems sub-optimal; curious to hear your thoughts? that said, it's understandable why they used MSAs, even if it seems to hint at winning CASP more than developing a generalizable model. arguably, MSA-dependence is the wi…

My realizations happened after my PhD. When I was writing my PhD I still believed we would solve the protein folding and structure prediction problems using classical empirical force fields.

It wasn't until I started my postdocs, where I started learning about protein evolutionary relationships (and competing in CASP), that I changed my mind. I wouldn't say it so much as "multiple sequence alignments"; those are just tools to express protein relationships in a structured way.

If Alphafold now, or in the future, requires no evolutionary relationships based on sequence (uniprot) and can work entirely by training on just the proteins in PDB (many of which are evoutionarily related) and still be able to predict novel folds, it will be very interesting times. The one thing I have learned is that evolutionary knowledge makes many hard problems really easy, because you're taking advantage of billions of years of nature and an easy readout.

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

#304

Post by nextos »

panarky wrote: post #263
dekhn wrote: post #200

Earlier quoted context omitted.

That's the aspirational goal. And I would say that it's a bit of an inflexible one- for example, if we had an ML that could generate molecules that cure diseases that would pass FDA approval, I wouldn't really care if scientists couldn't explain the underlying principles. But I'm an ex-scientist who is now an engineer, because I care more about tools that produce useful predictions than understanding underlying princ…

It's not either-or, it's yes-and. We don't have to abandon one for the other. AlphaFold 3 can rapidly reduce a vast search space in a way physically-based methods alone cannot. This narrowly focused search space allows scientists to apply their rigorous, explainable, physical methods, which are slow and expensive, to a small set of promising alternatives. This accelerates drug discovery and uncovers insights that wou…

Or you can treat AlphaFold as a black box / oracle and work at systems biology level, i.e. at pathway and cellular level. Protein structures and interactions are always going to be hard to predict with interpretable models, which I also prefer.

My only worry is that AlphaFold and others, e.g. ESM, seem to be bit fragile for out-of-distribution sequences. They are not doing a great job with unusual sequences, at least in my experience. But hopefully they will improve and provide better uncertainty measures.

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

#305

Post by MobiusHorizons »

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…

Is it capable of predictions though? Ie can it accurately predict the folding of new molecules? Otherwise how do you distinguish accuracy from overfitting.

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

#306

Post by slashdave »

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 terms of docking, you can call the conventional approaches "physically-based", however, they are rather poor physical models. Namely, they lack proper electrostatics, and, most importantly, basically ignore entropic contributions. There is no reason for concern.

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

#307

Post by slashdave »

slibhb wrote: post #188
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.

Not comparable. Our current knowledge of the physics involved in these systems is complete. It is just impossibly difficult to calculate from first principles.

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

#308

Post by trueismywork »

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…

To paraphrase Kahan, it's not interesting to me whether a method is accurate enough or not, but whether you can predict how accurate you can be. So, if ML methods can predict that they're right 98% of times then we can build this in our systems, even if we don't understand how they work.

Deterministic methods can predict result with a single run, ML methods will need ensemble of results to show the same confidence. It is possible at the end of day that the difference in cost might not he that high over time.

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

#310

Post by dekhn »

AtlasBarfed wrote: post #271
dekhn wrote: post #25

Earlier quoted context omitted.

AlphaFold has been widely validated- it's now appreciated that its predictions are pretty damn good, with a few important exceptions, instances of which are addressed with the newer implementation.

"pretty damn good" So... what percentage of the time? If you made an AI to pilot an airplane, how would you verify its edge conditions, you know, like plummeting out of the sky because it thought it had to nosedive? Because these AIs are black box neural networks, how do you know they are predicting things correctly for things that aren't in the training dataset? AI has so many weasel words.

As mentioned elsewhere and this thread and trivially determinable by reading, AF2 is constantly being evaluated in blind predictions where the known structure is hidden until after the prediction. There's no weasel here; the process is well-understood and accepted by the larger community.
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