Folding@home uses Rosetta, a physics-based approach that is outperformed by deep learning methods such as AlphaFold2/3.

Folding@home uses Rosetta only to generate initial conformations[1], but the actual simulation is based on Markov State Models. Note that there is another distributed computing project for Rosetta, Rosetta@home.

[1]: https://foldingathome.org/dig-deeper/#:~:text=employing%20Ro...

Yes, all data Google used was public. We have enough compute from YC (thanks YC!) to do this. The main thing is the technical infrastructure - processing the data, efficient loading at training time, proper benchmarking, etc. We are building these now.

Thanks for the answer! It's much better to have the definitive answer rather than rely on gut feeling (even though it was right in this case).

Keep up the good work!

How much compute does YC give you access to btw? Is that just things like azure credit or do YC have actual hardware?

Thanks!

The predictions can be verified by comparing the predicted structure to the experimentally solved structure, either crystal or cryoEM. The model is still training and improving, we will release the benchmarking results after it's complete.

Amazing! What kind of things did you work on?

My job was mostly mundane machine learning: classification over very large categorical sets.

I never had anything more than a dim intuition of the serious chemistry going on before the bytes got to me.

Where were you working? That sounds super interesting

I’m a big fan of what you folks are doing by the way.

Haskell (and Nix) people are fond of talking about “constraints as power”.

https://github.com/Ligo-Biosciences/AlphaFold3/blob/ebdf3b12...

I was a contractor for like a month so I’m not at liberty to talk about the details.

There are a number of companies doing innovative things around quantifying proteins and their concentrations in various samples.

I had the privilege to rub elbows with folks working on such cool stuff.

Yes this is a good point. We are actively speaking with our counsel to check this. Thanks for flagging, though.

OpenFold, which was AlphaFold2's open-source implementation was published in Nature Methods. We will prepare a similar publication once the model is more mature and when we have a nice set of experiments showing the model's interesting properties.

We think enzymes are super cool! You can build molecular assembly lines at the atomic scale with them. Many pharmaceuticals are already manufactured with enzymes such as the diabetes drug Januvia. Engineering them is a big bottleneck though - takes years and millions of dollars. We want to speed this up with AI-powered design. Next step is ligand-protein prediction capability of AlphaFold3, which is also super useful for modelling enzyme-substrate interactions.

Our long term goal is to design enzymes for chemical manufacturing. We decided to build AlphaFold3 because we had seen how useful AlphaFold2 had been for the protein design field. No one else was building it fast enough for us, so we decided we should do it ourselves. We are committed to training and open-sourcing the full version with ligand and nucleic acid prediction capabilities as well since it is so useful for the biotech industry.