now that we have this inevitable discussion venue after everyone has digested the PR (and some of the people her might have even had a chance to ask some questi

now that we have this inevitable discussion venue after everyone has digested the PR (and some of the people her might have even had a chance to ask some questions to deepmind) some remarks (questions - if anyone from the team is here): - I thought that open code was becoming the standard and also being pushed by Google? But apparently this does not apply to deepmind, because of $$$s? For the original Alphafold (which actually was 3 models) no code is available except, where it had to be (their nature publication on one of the three models used in the competition). - why did they not participate with all the available proteins? I guess it's some loophole in the rules, to allow for greater "improvements" even when models are not super general, but from a naive scientific view that is absolutely stupid. - maybe they went fully generative this time, but in CASP13, 2/3 of their models were just blackbox-predictors, which they optimized with simulated annealing. Given that the configurational sample space for the protein is huge, doing that seems still rather costly. I wonder how the actual spectra fitting works and compares to that and why experimentalists could not go this route as well? (just do simulated annealing, until the spectrum fits). - they already trained on all known proteins, yet with some they are still far off. Seems like it's not solved really, though results are certainly impressive and it could be a great tool for any person interested in frozen protein structures.