AI Revolutionized Protein Science, but Didn't End It

Post by dekhn » Thu, Jun 27, 2024, 7:01 PM UTC

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'…

Post by eru » Fri, Jun 28, 2024, 2:44 AM UTC

trivexwe wrote: post #28

eru wrote: post #11

Earlier quoted context omitted.

> 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…

Post by eru » Sun, Jun 30, 2024, 7:25 AM UTC

dekhn wrote: post #31

eru wrote: post #4

Earlier quoted context omitted.

> 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'…

The working hypothesis you described was considered fairly obsolete some time ago. The current model is much more "most proteins fold to kinetically accessible states". The assumption of global lowest energy led to a lot of wasted effort and misled computer scientists. But along the way to this understanding we learned an awful lot about the forces that affect folding- see for example "hydrophobic collapse".

Post by DrScientist » Mon, Jul 01, 2024, 10:16 AM UTC

mensetmanusman wrote: post #27

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.

Post by COGlory » Tue, Jul 02, 2024, 8:58 PM UTC

savingsPossible wrote: post #20

nybsjytm wrote: post #5

Great article, covers well both the achievements and the shortcomings. It's crazy how many people write about these kinds of AI developments while completely skipping over anything like the following: > The “good news is that when AlphaFold thinks that it’s right, it often is very right,” Adams said. “When it thinks it’s not right, it generally isn’t.” However, in about 10% of the instances in which AlphaFold2 was “v…

> 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) I wonder what that confidence score means... If it is 90% probability, then we'd expect it to be wrong 10% of the time