Not solved, no. Definitely much much better than before. The difference AlphaFold made is significant: we’re talking about getting a decent model in a couple minutes using a PC compared to several months of calculations before.
However we still need experimental data: in many occasions AlphaFold gives an incorrect model. With some experimental data that model can be improved, but we still have no reliable way to know what the structure of a protein is starting from the amino acidic sequence without extensive experimentation.
That’s the big promise of quantum computers, there are however two major problems in my opinion:
There’s still no theoretical framework which explains how once we have a quantum computer we may tackle protein folding
Plenty quantum computing companies closed shortly after AlphaFold was published since they lost all funding because protein folding was “solved”
The interesting part would be to a reliable and computationally accessible way to handle disordered proteins. Seeing how they can move could be quite revolutionary.
I had to work on some disordered proteins and you’re pretty much just guessing, plausibly you’re better off going to a casino blindfolded and play blackjack.
I doubt there is a comparable correctness metric between LLMs and protein structure prediction models.
You can measure how many times they correctly predict a thing, but results will greatly change according to what your objective is. Those are only comparable when you’re trying to predict the same thing.
As such my reply would be: sometimes more incorrect sometimes more correct. However, in general, a mishandled incorrect protein structure prediction is way more expensive than an LLM hallucination.
Wait I thought protein folding was “solved”? Is it just significantly improved or was I lied to?
Not solved, no. Definitely much much better than before. The difference AlphaFold made is significant: we’re talking about getting a decent model in a couple minutes using a PC compared to several months of calculations before.
However we still need experimental data: in many occasions AlphaFold gives an incorrect model. With some experimental data that model can be improved, but we still have no reliable way to know what the structure of a protein is starting from the amino acidic sequence without extensive experimentation.
That’s the big promise of quantum computers, there are however two major problems in my opinion:
Gotcha, thank you ❤️
Didn’t google shut down alphafold recently?
How do you solve protein folding when there are so many different ways for proteins to arrange?
A reliable way to simulate any kind of protein fold. As long as you have a method you could hypothetically “solve” the issue
You dont solve anything. You generate models. That’s it. Without experimental validation they are just cartoons.
Then why has modelling been so focused on?
There actually aren’t. However, some of the most interesting proteins have no structure at all.
The interesting part would be to a reliable and computationally accessible way to handle disordered proteins. Seeing how they can move could be quite revolutionary.
I had to work on some disordered proteins and you’re pretty much just guessing, plausibly you’re better off going to a casino blindfolded and play blackjack.
We traded it in for something that will hallucinate a solution.
AlphaFold is incorrect in plenty occasions.
More incorrect than a text prediction algorithm?
I doubt there is a comparable correctness metric between LLMs and protein structure prediction models.
You can measure how many times they correctly predict a thing, but results will greatly change according to what your objective is. Those are only comparable when you’re trying to predict the same thing.
As such my reply would be: sometimes more incorrect sometimes more correct. However, in general, a mishandled incorrect protein structure prediction is way more expensive than an LLM hallucination.
Ah, yes I can imagine that being the case