>It really doesn't seem like you are well versed on this topic. I don't know any other way to put it. Ok. I am, but I don't expect you to take my word for it.

>It really doesn't seem like you are well versed on this topic. I don't know any other way to put it. Ok. I am, but I don't expect you to take my word for it. >alphafold's 90% is 90% because that is the highest possible score with the level of error within the source material, not because we know there is 10% error left to "fix". This is just making my point. Homology modeling is not knowledge of protein folding. It's knowledge of structures of experimentally observed folded proteins >which is the opposite of what we've seen from alphafold. 90% accuracy has to be good enough to be useful as it already has been pointed out, that's within the error range of x-ray crystallography I dont think you really understand how protein model building works. You are conflating prediction error from a model with observed error from empirical data. An xray structure with an Rfree of 10% is not the same as a predictive model that is 90% accurate, because in one case you have electron density to guide you, and in the other you do not. >so let's be clear here, any "crisis of confidence" here isn't "this isn't precise enough to work" but "I don't trust dem neural networks, they are going to take our jobs". Unnecessarily derogatory