AlphaFold

Seems like DeepMind just caused the ImageNet moment for protein folding.

Blog post isn't that deeply informative yet (paper is promised to appear soonish). Seems like the improvement over the first version of AlphaFold is mostly usage of transformer/attention mechanisms applied to residue space and combining it with the working ideas from the first version. Compute budget is surprisingly moderate given how crazy the results are. Exciting times for people working in the intersection of molecular sciences and ML :)

Tweet by Mohammed AlQuraishi (well-known domain expert)

DeepMind BlogPost

UPDATE:
Nature published a comment on it as well

The model itself is closed source and heavily restricted. Yes, the dataset of all known proteins is available, but Deepmind has restricted the prediction of custom proteins, predictions of these custom proteins binding to molecules, custom ligands, quaternary structures of custom protein, and many more.

The model can only be accessed on the cloud using AlphaFold server and each user is limited to 10 predictions per day. Plus, the model cannot be used for commercial purposes nor can predictions for the structures of proteins bound to drugs be made. Here’s an extract from the magazine:

Isomorphic Labs, a DeepMind spin-off company in London, is using AlphaFold3 to develop drugs, both through its own pipeline and with other pharmaceutical companies. “We have to strike a balance between making sure that this is accessible and has the impact in the scientific community as well as not compromising Isomorphic’s ability to pursue commercial drug discovery,” says Pushmeet Kohli, DeepMind’s head of AI science and a study co-author.

This is a huge departure from AlphaFold 2, where the weights were shared openly and anyone is allowed to modify and use the model on their local servers. The cynical side of me thinks that with the route in which Deepmind is currently headed, they intend to monopolize a significant chunk of the drug discovery process and keep this valuable tool from us plebes who couldn’t be trusted with this technology (yes, only molecular biologists know how to use this tool at the moment, but what about the future when AGI arrives to explain it to us? Will some information simply be considered too “hazardous” just because some non-democratic organization that doesn’t represent our interests decides that we cannot access them?)

Look, I’m not against companies making money from their products, but maybe closing off the model to this extent isn’t the most productive thing to do. Journals have gate-kept so much knowledge from the people who couldn’t access the open research in order to make a big buck, maybe we shouldn’t do the same to AI. I believe that all scientific progress should be free for all those interested to view and build upon, rather than be restricted to the whims of a company that could place its commercial ambitions above those of the greater public. I hope I’m not alone in this department.

Hi **,**

I'm curious about AlphaFold and its applications, especially in wet lab work. Has anyone in the community worked with AlphaFold, and what has your experience been? I'm particularly interested in understanding the delimitation of AlphaFold and exploring potential features that could be added for broader use. Any insights or suggestions would be greatly appreciated!

"Last year we presented #AlphaFold v2 which predicts 3D structures of proteins down to atomic accuracy. Today we’re proud to share the methods in @Nature w/open source code. Excited to see the research this enables. More very soon!"

I did not see this one coming, I got to admit it.