> Because 5-10 angstrom resolution isn't terribly hard to do either with traditional biochemistry, or with homology modeling, with very few exceptions here. The

> Because 5-10 angstrom resolution isn't terribly hard to do either with traditional biochemistry, or with homology modeling, with very few exceptions here. The problem I have is that the confidence level there isn't good enough for the actual work that matters. Exact ligand interactions, etc. It really doesn't seem like you are well versed on this topic. I don't know any other way to put it. Before alphafold, the best modeling methods would hit ~75% accuracy, and x-ray crystallography is both prohibitively expensive and also specimen specific in regards to it's efficacy or even possibility. So what alphafold can do, "traditional biochemistry, or with homology modeling" can not touch and I don't know why you are claiming they can nor what you are basing that claim off of. I feel like I'm repeating myself and I wish I didn't have to, but, 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". > The problem I have is that the confidence level there isn't good enough for the actual work that matters. Yeah, that is actually how the world works. Confidence starts low then builds upon successful results. It's really not worth bringing up unless we start to see a string of failures from alphafold's prediction ability, 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 , the current "Gold standard", 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".