Probably worth mentioning that David Baker’s lab released a similar model (predicts protein structure along with bound DNA and ligands), just a couple of months ago, and it is open source [1].
It’s also worth remembering that it was David Baker who originally came up with the idea of extending AlphaFold from predicting just proteins to predicting ligands as well [2].
Unlike AlphaFold 3, which predicts only a small, preselected subset of ligands, RosettaFold All Atom predicts a much wider range of small molecules. While I am certain that neither network is up to the task of designing an enzyme, these are exciting steps.
One of the more exciting aspects of the RosettaFold paper is that they train the model for predicting structures, but then also use the structure predicting model as the denoising model in a diffusion process, enabling them to actually design new functional proteins. Presumably, DeepMind is working on this problem as well.
I appreciated this, but it's probably worth mentioning: when you say AlphaFold 3, you're talking about AlphaFold 2.
TFA announces AlphaFold 3.
Post: "Unlike AlphaFold 3, which predicts only a small, preselected subset of ligands, RosettaFold All Atom predicts a much wider range of small molecules"
TFA: "AlphaFold 3...*models large biomolecules such as proteins, DNA and RNA*, as well as small molecules, also known as ligands"
Post: "they also use the structure predicting model as the denoising model in a diffusion process...Presumably, DeepMind is working on this problem as well."
TFA: "AlphaFold 3 assembles its predictions using a diffusion network, akin to those found in AI image generators."
Coming up with ideas is cheaper than executing the ideas. Predicting a wide range of molecules okay-ish is cheaper than predicting a small range of molecules very well.
It’s also worth remembering that it was David Baker who originally came up with the idea of extending AlphaFold from predicting just proteins to predicting ligands as well [2].
1. https://github.com/baker-laboratory/RoseTTAFold-All-Atom
2. https://alexcarlin.bearblog.dev/generalized/
Unlike AlphaFold 3, which predicts only a small, preselected subset of ligands, RosettaFold All Atom predicts a much wider range of small molecules. While I am certain that neither network is up to the task of designing an enzyme, these are exciting steps.
One of the more exciting aspects of the RosettaFold paper is that they train the model for predicting structures, but then also use the structure predicting model as the denoising model in a diffusion process, enabling them to actually design new functional proteins. Presumably, DeepMind is working on this problem as well.