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AI Revolutionized Protein Science, but Didn't End It

quantamagazine.org

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Re: AI Revolutionized Protein Science, but Didn't End It

#21

Post by spywaregorilla »

eru wrote: post #4
trivexwe wrote: post #3

Weird article. It mentions multiple times that ~”the protein folding problem is solved” as well as multiple instances of ~”but there are limitations to this technique and it is often missing crucial details”. It really is difficult to conceptualize these highly nonlinear problem spaces, like protein folding, until you attempt to work with them. Many in software development have an intuitive understanding of the diffi…

> Protein folding is still an unsolved problem, and I’m dubious of the notion machine learning will ever solve it, but hopefully we get some helpful science out of it. As a working hypothesis, protein folding assumes that a protein folds into the globally lowest energy configuration. And that's a good assumption for a start. However, nature isn't magic and can't magically solve global optimisation problems. If there'…

I feel like there should be a much stronger effort to solve optimization problems with ML enabled guesses. It's arguably the most important problem to be solving to improve ML itself.

Humans, for example, can provide extremely strong guesses by just eyeballing travelling salesmen problems without doing any calculations. If we could use ML to take a problem and guess how to reformulate it with 95% of the search space cut out, we would be in a much stronger place. My gut says this should be theoretically possible and is probably the mechanism that under the hood biological learning systems use to such a great effect that its ok to just use greedy and less efficient methods to do last mile of optimization without something like backprop.

Re: AI Revolutionized Protein Science, but Didn't End It

#22

Post by Mikhail_K »

ak_111 wrote: post #14

"...Some cell biologists and biochemists who used to work with structural biologists have replaced them with AlphaFold2 — and take its predictions as truth. Sometimes scientists publish papers featuring protein structures that, to any structural biologist, are obviously incorrect, Perrakis said. “And they say: ‘Well, that’s the AlphaFold structure.’”" It is amazing that this happens. I am not naive about academic sta…

The article states: "However, in about 10% of the instances in which AlphaFold2 was “very confident” about its prediction (a score of at least 90 out of 100 on the confidence scale), it shouldn’t have been, he reported: The predictions didn’t match what was seen experimentally."

This is the number one issue with using the so-called "deep learning": the results may be completely wrong and there is no known way to predict when they will be or detect when they are (relying on "deep learning" alone).

The worse issue is that by "deep learning" we learn only the coefficients that give accurate predictions on a training set. Extrapolating the results is the hopeful leap of faith that is known to break down catastrophically on some inputs. The "neural nets" do not give us the new knowledge, but rather, an attractive nuisance of a tool.

Re: AI Revolutionized Protein Science, but Didn't End It

#23

Post by Mikhail_K »

jagrsw wrote: post #19
TheBozzCL wrote: post #7

Earlier quoted context omitted.

Tale as old as ML: people don’t understand it’s an assistance tool, and instead assume it’s always right. Crazy how we tend to wave away even errors rates of 1% or less. One in a thousand is a lot.

This tool is designed to be helpful right now . Looking ahead, there's no reason why AI can't eventually match, or even surpass, human intelligence across the board. Whether it's advancements in LLMs, with features like long-term memory, or breakthroughs in other areas of ml, it's not guaranteed that humans will remain needed in the research process.

> Looking ahead, there's no reason why AI can't eventually match, or even surpass, human

> intelligence across the board.

There is a reason, actually: what is presently called an "AI" has no concern for the truth. It is a bullshit machine that aims to mimic the right answer.

Re: AI Revolutionized Protein Science, but Didn't End It

#24

Post by bamboozled »

Mikhail_K wrote: post #22
ak_111 wrote: post #14

"...Some cell biologists and biochemists who used to work with structural biologists have replaced them with AlphaFold2 — and take its predictions as truth. Sometimes scientists publish papers featuring protein structures that, to any structural biologist, are obviously incorrect, Perrakis said. “And they say: ‘Well, that’s the AlphaFold structure.’”" It is amazing that this happens. I am not naive about academic sta…

The article states: "However, in about 10% of the instances in which AlphaFold2 was “very confident” about its prediction (a score of at least 90 out of 100 on the confidence scale), it shouldn’t have been, he reported: The predictions didn’t match what was seen experimentally." This is the number one issue with using the so-called "deep learning": the results may be completely wrong and there is no known way to pred…

This seems like it should be bigger news than it is?

Re: AI Revolutionized Protein Science, but Didn't End It

#25

Post by exmadscientist »

eru wrote: post #17
gabia wrote: post #16

Earlier quoted context omitted.

Great points about the energy minimisation issue. Funnily enough, this is actually a problem with de-novo protein design at the moment: the designed proteins are _too_ stable! Compared to natural proteins. Protein are often not static shapes, they are machines that need to be dynamic - in other words what you said, they do not live at some deep global optimum.

Interesting point! > [...] in other words what you said, they do not live at some deep global optimum. I think what you said only depends on the minimum being relatively flat (instead of deep); but it doesn't matter whether it's global or local.

> I think what you said only depends on the minimum being relatively flat (instead of deep); but it doesn't matter whether it's global or local

No. There is no such thing as a "global minimum" energy conformation, because the conditions vary wildly. Many protein structure changes are brought about by changes in the local chemical potentials and even electric fields. This is not something you can get a good grip on by thinking in terms of "flat minima".

Re: AI Revolutionized Protein Science, but Didn't End It

#26

Post by DrScientist »

It is/was a brilliant piece of work ( Nobel prize level ) - however I think the impact is over-hyped - as somebody who has experimentally solved a protein structure - I can tell you knowing the structure doesn't necessarily help you understand the biology - not every structure is as functionally obvious as the structure of DNA for example.

In terms of drug discovery - even assuming the models are as good as experimental structures, you only get the same benefit as experimental structures - which have helped small molecule drug discovery - but I would argue not transformed it. All the existing challenges with structure based drug design remain.

BTW while Alphafold 2 was a big step forward from Alphafold(1) - it wasn't a complete shock as Alphafold 1 had already topped the charts in a previous competition a couple of years earlier.

Re: AI Revolutionized Protein Science, but Didn't End It

#27

Post by mensetmanusman »

DrScientist wrote: post #26

It is/was a brilliant piece of work ( Nobel prize level ) - however I think the impact is over-hyped - as somebody who has experimentally solved a protein structure - I can tell you knowing the structure doesn't necessarily help you understand the biology - not every structure is as functionally obvious as the structure of DNA for example. In terms of drug discovery - even assuming the models are as good as experimen…

“ knowing the structure doesn't necessarily help you understand the biology ”

I think most everyone recognizes this, but also believes it is probably necessary to know the structure to understand the biology in the future. Ie a necessary yet insufficient advancement that is Nobel worthy.

Re: AI Revolutionized Protein Science, but Didn't End It

#28

Post by trivexwe »

eru wrote: post #11
trivexwe wrote: post #8

Earlier quoted context omitted.

> Why are you dubious? Where do your objections come from? That the results the machine learning techniques provide are still nondeterministic. Meaning that they are, in terms of identifying other local minima that satisfy the constraints, as good as a guess. If the provided solution also came with a method of systemic modification to derive all other solutions that satisfy the constraints, then I would be satisfied.…

> That the results the machine learning techniques provide are still nondeterministic. I think I know what you are trying to say, but 'determinism' or not isn't the problem. You can run machine learning methods completely deterministically: just use a pseudo-random-number-generator (and be careful about how you seed it, and be wary of the problems with concurrency etc). > If the provided solution also came with a met…

> Sounds like woo.

O, definitely woo. I tried to make that explicitly clear by using “hypothesis” and “appearance”.

My hypothesis is less “optimization solutions == consciousness” and more positing that our brains, “action potentials” was meant as cheeky shorthand for the human brain, use an “optimization solution” that we identify as “consciousness”, or as you put it “sentience”.

But to quote South Park, “and I base that on absolutely nothing”. ;P

Re: AI Revolutionized Protein Science, but Didn't End It

#29

Post by olddustytrail »

ben_w wrote: post #13
TheBozzCL wrote: post #7

Earlier quoted context omitted.

Tale as old as ML: people don’t understand it’s an assistance tool, and instead assume it’s always right. Crazy how we tend to wave away even errors rates of 1% or less. One in a thousand is a lot.

It really depends on the context; (Some) LLMs look impressive specifically because the error rate is comparable to a high score on an exam… the mistake is that even if it was a straight-A student (it's too alien to be that) then it would still only be a student, and we don't put fresh graduates in charge of everything important. I don't have the domain knowledge to even guess how good 90% is in molecular biology rese…

> we don't put fresh graduates in charge of everything important.

We put dribbling halfwit morons in charge of everything. I'm thinking of Liz Truss as UK Prime Minister, but I'm sure most countries have their own examples.

Re: AI Revolutionized Protein Science, but Didn't End It

#30

Post by dekhn »

trivexwe wrote: post #3

Weird article. It mentions multiple times that ~”the protein folding problem is solved” as well as multiple instances of ~”but there are limitations to this technique and it is often missing crucial details”. It really is difficult to conceptualize these highly nonlinear problem spaces, like protein folding, until you attempt to work with them. Many in software development have an intuitive understanding of the diffi…

This is a bit pedantic, but: AF says little to nothing about protein "folding". It is focused on static structure prediction. The history of this is a bit muddy but if you follow the details carefully you'll see that "protein folding" is a term that references the physical process by which proteins adopt their "final" conformations (or more accurately, interconvert between a bunch of accessible conformational states), while static structure prediction only cares about the final conformational state (possibly states).

Although many people say "protein folding problem" that's really referring to a different and far more complex problem than static structure prediction. What is the exact trajectory that a protein follows when moving from the fully unfolded state to the final states? What forces dominate that process? How do proteins overcome large barriers so quickly? To what extent does the cost of interacting with water dominate? What are the rates at which fully folded proteins interconvert between substates? Which proteins will never fold on their own, why, and how do they get folded by other proteins?

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