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I wonder what the license for RoseTTAFold is. On github you have:
https://github.com/RosettaCommons/RoseTTAFold/blob/main/LICE...
But there's also:
https://files.ipd.uw.edu/pub/RoseTTAFold/Rosetta-DL_LICENSE....
Which is it?
> Perhaps the opportunity here is to provide a quicker feedback loop for theory about predictions in the real world. Almost like unit tests.
Or jumping the gap entirely to move towards more self-driven reinforcement learning.
Could one structure the training setup to be able to design its own experiments, make predictions, collect data, compare results, and adjust weights...? If that loop could be closed, then it feels like that would be a very powerful jump indeed.
In the area of LLMs, the SPAG paper from last week was very interesting on this topic, and I'm very interested in seeing how this can be expanded to other areas:
https://github.com/Linear95/SPAG
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
The original NNUE paper cites AlphaZero[0]. The architectures are different because NNUE is optimized for CPUs and uses integer quantization and a much smaller network. I don't think one could credibly claim that it would have come about if not for Google making so much noise about their neural network efforts in Go, Chess and Shogi.
0: https://github.com/asdfjkl/nnue/blob/main/nnue_en.pdf
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