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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

#101

Post by HanClinto »

boxed wrote: post #91
j7ake wrote: post #80

So it’s okay now to publish a computational paper with no code? I guess Nature’s reporting standards don’t apply to everyone. > A condition of publication in a Nature Portfolio journal is that authors are required to make materials, data, code, and associated protocols promptly available to readers without undue qualifications. > Authors must make available upon request, to editors and reviewers, any previously unrep…

Are you an editor or reviewer?

Good question.

Also makes me wonder -- where's the line? Is it reasonable to have "layperson" reviewers? Is it reasonable to think that regular citizens could review such content?

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

#102

Post by perihelions »

l33tman wrote: post #10
Metacelsus wrote: post #4

From: https://www.nature.com/articles/d41586-024-01383-z >Unlike RoseTTAFold and AlphaFold2, scientists will not be able to run their own version of AlphaFold3, nor will the code underlying AlphaFold3 or other information obtained after training the model be made public. Instead, researchers will have access to an ‘AlphaFold3 server’, on which they can input their protein sequence of choice, alongside a selection of…

That sucks a bit. I was just wondering why they are touting that 3rd party company in their own blog post, who commercialise research tools, as well. Maybe there are some corporate agreements with them that prevents them from opening the system... Imagine the goodwill for humanity for releasing these pure research systems for free. I just have a hard time understanding how you can motivate to keep it closed. Let's ho…

- "Imagine the goodwill for humanity for releasing these pure research systems for free."

The entire point[0] is that they want to sell an API to drug-developer labs, at exclusive-monopoly pricing. Those labs in turn discover life-saving drugs, and recoup their costs from e.g. parents of otherwise-terminally-ill children—again, priced as an exclusive monopoly.

[0] As signaled by "it is not possible to obtain structures of proteins bound to possible drugs"

It's a massive windfall for Alphabet, and it'd be a profound breach of their fiduciary duties as a public company to do anything other than lock-down and hoard this API, and squeeze it for every last billion.

This is a deeply, deeply, deeply broken situation.

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

#103

Post by insane_dreamer »

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).

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

#105

Post by dwroberts »

Metacelsus wrote: post #4

From: https://www.nature.com/articles/d41586-024-01383-z >Unlike RoseTTAFold and AlphaFold2, scientists will not be able to run their own version of AlphaFold3, nor will the code underlying AlphaFold3 or other information obtained after training the model be made public. Instead, researchers will have access to an ‘AlphaFold3 server’, on which they can input their protein sequence of choice, alongside a selection of…

This turns it into a tool that deserves to be dethroned by another group, frankly. What a strange choice.

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

#106

Post by tambourine_man »

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…

Our metaphors and intuitions were crumbling already and stagnating. See quantum physics: sometimes a particle, sometimes a wave, and what constitute a measurement anyway?

I’ll take prediction over understanding if that’s the best our brains can do. We’ve evolved to deal with a few orders of magnitude around a meter and a second. Maybe dealing with light-years and femtometer/seconds is too much to ask.

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

#107

Post by dyauspitr »

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…

Whatever it is if we needed to we could follow each instruction through the black box. It’s never going to be as opaque as something organic.

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

#108

Post by wslh »

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…

This is the topic of epistemology of the sciences in books such as "New Direction in the Philosophy of Mathematics" [1] and happened before with problems such as the four color theorem [2] where AI was not involved.

Going back to the uninterpretable ML models in the context of AlphaFold 3, I think one method for trying to explain the findings is similar to the experimental methods of physics with reality: you perform experiments with the reality (in this case AlphaFold 3) to came up with sound conclusions. AI/ML is an interesting black-box system.

There are other open discussions on this topic. For example, can our human brain absorbe that knowledge or it is limited somehow with the scientific language that we have now?

[1] https://www.google.com.ar/books/edition/New_Directions_in_th...

[2] https://en.wikipedia.org/wiki/Four_color_theorem

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

#109

Post by torrefatto »

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…

You are conflating the whole scientific endeavor to a very specific problem to which this specific approach is effective at producing results that fit with the observable world. This has nothing to do with science as a whole.

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

#110

Post by dekhn »

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 computational docking. Currently structures rarely capture the salient details required to identify a molecule that has precisely the biological outcome desired, because the biological outcome is an extremely complex function that incorporates a wide array of other details, such as other proteins, metabolism, and more.

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