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].

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.

Important caveat: it's only about 70% accurate. Why doesn't the press release say this explicitly? It seems intentionally misleading to only report accuracy relative to existing methods, which apparently are just not so good (30%, 50% in various settings). https://www.fastcompany.com/91120456/deepmind-alphafold-3-dn...

Very sad to see they did not make it open source. When you have a technology that has the potential to be a gateway for drug development, to the cures of new diseases, and instead you choose to make it closed, it is a very huge disservice to the community at large. Sure, release your own product alongside it, but making it closed source does not help the scientific community upon which all these innovations were built. Especially if you have lost a loved one to a disease which this technology will one day be able to create cures for, it is very disappointing.

> What is different about the new AlphaFold3 model compared to AlphaFold2?

> AlphaFold3 can predict many biomolecules in addition to proteins. AlphaFold2 predicts structures of proteins and protein-protein complexes. AlphaFold3 can generate predictions containing proteins, DNA, RNA, ions,ligands, and chemical modifications. The new model also improves the protein complex modelling accuracy. Please refer to our paper for more information on performance improvements.

AlphaFold 2 generally produces looping “ribbon-like” predictions for disordered regions. AlphaFold3 also does this, but will occasionally output segments with secondary structure within disordered regions instead, mostly spurious alpha helices with very low confidence (pLDDT) and inconsistent position across predictions.

So the criticism towards AlphaFold 2 will likely still apply? For example, it’s more accurate for predicting structures similar to existing ones, and fails at novel patterns?

As a software engineer, I kind of feel uncomfortable about this new model. It outperforms Alphafold 2 at ligand binding, but Alphafold 2 also had some more hardcoded and interpretable structural reasoning baked into the model architecture.

There's so many things you can incorporate into a protein folding model such as structural constraints, rotational equivariance, etc, etc

This new model simple does away with some of that, achieving greater results. And the authors simply use distillation from data outputted from Alphafold2 and Alphafold2-multimer to get those better results for those cases where you wind up with implausible results.

You have to run all those previous models, and output their predictions to do the distillation to achieve a real end-to-end training from scratch for this new model! Makes me feel a bit uncomfortable.