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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

#151
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 vivo in humans in the cloud

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

#152

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…

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

#153
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…

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

#154
post #120

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 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…

Medicine can be explained fairly simply, and the why of how it works as it does is also explained by this:

Imagine a very large room that has every surface covered by on-off switches.

We cannot see inside of this room. We cannot see the switches. We cannot fit inside of this room, but a toddler fits through the tiny opening leading into the room. The toddler cannot reach the switches, so we equip the toddler with a pole that can flip the switches. We train the toddler, as much as possible, to flip a switch using the pole.

Then, we send the toddler into the room and ask the toddler to flip the switch or switches we desire to be flipped, and then do tests on the wires coming out of the room to see if the switches were flipped correctly. We also devise some tests for other wires to see if that naughty toddler flipped other switches on or off.

We cannot see inside the room. We cannot monitor the toddler. We can't know what _exactly_ the toddler did inside the room.

That room is the human body. The toddler with a pole is a medication.

We can't see or know enough to determine what was activated or deactivated. We can invent tests to narrow the scope of what was done, but the tests can never be 100% accurate because we can't test for every effect possible.

We introduce chemicals then we hope-&-pray that the chemicals only turned on or off the things we wanted turned on or off. Craft some qualifications testing for proofs, and do a 'long-term' study to determine if there were other things turned on or off, or a short circuit occurred, or we broke something.

I sincerely hope that even without human understanding, our AI models can determine what switches are present, which ones are on and off, and how best to go about selecting for the correct result.

Right now, modern medicine is almost a complete crap-shoot. Hopefully modern AI utilities can remedy the gambling aspect of medicine discovery and use.

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

#155
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…

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 does not allow for our symbol-based, causal reasoning mode. That is on us, not our capabilities or the universe.

All our theories are built on observation, so these empirical models yielding such useful results is a great thing - it satisfies the need for observing and acting. Missing explainability of the models merely means we have less ability to act more precisely - but it does not devalue our ability to act coarsely.

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

#156

This reminds me of Google’s claim that another “AI” discovered millions of new materials. The results turned out to be a lot of useless noise but that was only apparent after actual expert spent hundreds of hours reviewed the results[0] 0: https://www.404media.co/google-says-it-discovered-millions-o...

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

#157
post #148

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…

The success of these ML models has me wondering if this is what Quantum Mechanics is. QM is notoriously difficult to interpret yet makes amazing predictions. Maybe wave functions are just really good at predicting system behavior but don't reflect the underlying way things work. OTOH, Newtonian mechanics is great at predicting things under certain circumstances yet, in the same way, doesn't necessarily reflect the un…

That’s what thermodynamics is - we initially only had laws about energy/heat flow, and only later we figured out how statistical particle movements cause these effects.

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

#158

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 could be entering a new age of epicycles - high accuracy but very flawed understanding.

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

#159
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…

Nature has long been willing to break its own rules to be at the forefront of publishing new science.

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

#160
post #99

Earlier quoted context omitted.

> Does this decouple progress from our current understanding of the scientific process? Thank God! As a person who uses my brain, I think I can say, pretty definitively, that people are bad at understanding things. If this actually pans out, it means we will have harnessed knowledge/truth as a fundamental force, like fire or electricity. The "black box" as a building block.

This type of thing is called an "oracle". We've had stuff like this for a long time. Notable examples: - Temple priestesses - Tea-leaf reading - Water scrying - Palmistry - Clairvoyance - Feng shui - Astrology The only difference is, the ML model is really quite good at it.

> The only difference is, the ML model is really quite good at it.

That's the crux of it: we've had theories of physics and chemistry since before writing was invented.

None of that mattered until we came upon the ones that actually work.

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