Chemistry Nobel: Computational protein design and protein structure prediction • Hacker News Board

Re: Chemistry Nobel: Computational protein design and protein structure prediction

#341

Post by trott » Sun, Oct 13, 2024, 3:07 AM UTC

jhbadger wrote: post #334

trott wrote: post #333

Earlier quoted context omitted.

https://en.wikipedia.org/wiki/Functional_analog_(chemistry) explains the difference between structural and functional analogs: fentanyl is quite dissimilar from morphine, but binds the same targets.

Yes, the chemical structures can look very different when drawn in the 2D manner, but that's why 2D structures aren't very useful for understanding binding, much as primary sequences of proteins aren't that useful. Morphine and fentanyl bind to μ-opioid receptors, just like what naturally binds there (endorphins and enkephalin). But if they are binding to the same receptor, they have to have similar structures in the…

You originally wrote:

> very, very, few drugs are "novel" as opposed to being analogues of something naturally in the body

But "analog" means "structural analog" in this context (see https://en.wikipedia.org/wiki/Structural_analog ), which is why people disagreed with you, presumably.

It appears that you were merely saying that ligands must adopt a 3D conformation that's complementary to the receptor. Sure. That's the entire premise of molecular docking software.

But there can be very dissimilar ligands (like morphine and fentanyl) binding the same receptors. A major goal of drug discovery is to find such novel binders, not to regurgitate known ones.

Re: Chemistry Nobel: Computational protein design and protein structure prediction

#342

Post by make3 » Sun, Oct 13, 2024, 9:28 PM UTC

adastra22 wrote: post #254

roughly wrote: post #240

Earlier quoted context omitted.

It also proved that deep learning models are a valid approach to bioinformatics - for all its flaws and shortcomings, AlphaFold solves arbitrary protein structure in minutes on commodity hardware, whereas previous approaches were, well, this: https://en.wikipedia.org/wiki/Folding@home A gap between biological research and biological engineering is that, for bioengineering, the size of the potential solution space and…

AlphaFold doesn’t work for engineering though. Getting a shitty answer ends up being worse than useless. It seems to really accelerate productivity of researchers investigating bio molecules or molecules very similar to existing bio molecules. But not de novo stuff.

that's just not true. In a lot of cases in engineering, there are 10000000 possibilities, and deeplearning shows you 100 potentially more promising ones to double check, and that's worth huge amounts of money.

In a lot of cases deep learning is able to simulate complex system at a precision that is more than precise enough, that otherwise would not be tracktable (like is the case with alphafold), and again this is especially valuable if you can double check the output.

Ofc, in the field of language and vision and in a lot of other fields, deep learning is straight up the only solution.

Re: Chemistry Nobel: Computational protein design and protein structure prediction

#343

Post by LightBug1 » Fri, Oct 18, 2024, 9:31 AM UTC

throw310822 wrote: post #340

LightBug1 wrote: post #320

Earlier quoted context omitted.

The mental exercise is to compare two identical Jeff Bezos' (identify his attributes), one has the background/funds they did, one doesn't. Of course, that's not possible, so then you do the same with other highly intelligent and skilled tech professionals. I'd argue that without the funding and other resources, those skilled pro's won't get anywhere. But with it, some would do incredibly well. It's not common in a gl…

I think the question is not whether Bezos, or Gates, were helped by a reasonably wealthy family. The question is- was that wealth helpful because it allowed them to fully develop their own potential; or they have no merit at all and anybody with that wealth would have done the same? I think those who point out the privileged start of these entrepreneurs are suggesting the second, and yet that makes no sense (millions…

Disagree. I'm not suggesting the person in question does not have potential or merit and that anyone can do it. It's just that the wealth of a family can pour rocket fuel on that person to enable them to reach their potential.

Yes, that's debatable in each case, everyone has a different story. But it's very likely.

You do get the counterfactual, the plucky upstart who came from nothing. But I'd wager big that that's much rarer and more difficult.