4 ms·

> and the difference between 60% and 90% is a lot less critical than the difference between 90% and 100%. Hard disagree. It's common knowledge, and was even me

> and the difference between 60% and 90% is a lot less critical than the difference between 90% and 100%. Hard disagree. It's common knowledge, and was even mentioned in this fluff article, 90% accuracy is within the experimental error of the "truth set". It's entirely possible alphafold is more accurate then the X-ray crystallography source for a large portion of the data set. I would like to know what are you basing your very bold claim that 90 to 100% is bigger than 60 to 90%? Talking to random people in the pharmaceutical industry also seems like a very poor way to find truth. It's a very big field/industry with entrenched interests and very narrow fields of view. Also, roughly 100% of the companies who are asked, "are you about to be disrupted by new technology that you didn't create and is totally different then the way you do things?" answer, "Nope!".

COGlory 5y ago
>Hard disagree. It's common knowledge, and was even mentioned in this fluff article, 90% accuracy is within the experimental error of the "truth set". It's entirely possible alphafold is more accurate then the X-ray crystallography source for a large portion of the data set. Even if it was, we don't have a way to distinguish and so I'm not certain what extra good that does. >I would like to know what are you basing your very bold claim that 90 to 100% is bigger than 60 to 90%? 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.
- deeviant 5y ago
  > 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".
  - COGlory 5y ago
    >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
johncena33 5y ago
> Also, roughly 100% of the companies who are asked, "are you about to be disrupted by new technology that you didn't create and is totally different then the way you do things?" answer, "Nope!". I don't know anything about protein folding or drug development. But you brought up a very good point. Sometimes insiders and experts are the wrong people to ask, because they are not impartial. In the worst case we've seen so many virologists are on a campaign to paint lab leak as conspiracy theory just because their careers at risk. In similar vein it's also not the best idea to ask Wall St. who is responsible for 2008 financial crisis.

COGlory 5y ago

>Hard disagree. It's common knowledge, and was even mentioned in this fluff article, 90% accuracy is within the experimental error of the "truth set". It's entirely possible alphafold is more accurate then the X-ray crystallography source for a large portion of the data set. Even if it was, we don't have a way to distinguish and so I'm not certain what extra good that does. >I would like to know what are you basing your very bold claim that 90 to 100% is bigger than 60 to 90%? 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.

deeviant 5y ago
> 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".
- COGlory 5y ago
  >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

johncena33 5y ago

> Also, roughly 100% of the companies who are asked, "are you about to be disrupted by new technology that you didn't create and is totally different then the way you do things?" answer, "Nope!". I don't know anything about protein folding or drug development. But you brought up a very good point. Sometimes insiders and experts are the wrong people to ask, because they are not impartial. In the worst case we've seen so many virologists are on a campaign to paint lab leak as conspiracy theory just because their careers at risk. In similar vein it's also not the best idea to ask Wall St. who is responsible for 2008 financial crisis.