3 ms·
Is AlphaFold 1 or 2?
by AlexCoventry 7y ago
Is AlphaFold 1 or 2?
- ineedasername 7y agoI'm hardly the official judge of these things, but I would say it depends on how novel of an approach AlphaFold is to the problem. If it's a more efficient tool for doing the same things as before, I would put it towards the #1 end of the spectrum, unless it has also improved our basic understanding of folding or approaches to exploring the solution space of folded proteins, which would shift it towards #2. Personally I don't know enough about AlphaFold or the problems of protein folding to be remotely confident in my judgment on it
- jhrmnn 7y agoI have a decent understanding of the approach and would vote for #1. I’d say almost all applications of ML in physical sciences are #1. In contrast applying methods of statistical physics to understand how deep learning (as in DNN+SDG) works at all is a good example of #2.
- sjg007 7y agoNeural networks are differentiable regexes that can be trained from examples. In the alpha fold case, which is the case with a lot of bioinformatics actually, is that you don't need to know a lot about the biological domain to be successful in solving "data" problems in the field.
- Reelin 7y agoAccording to at least one expert in the field, it's a bit of both. (https://moalquraishi.wordpress.com/2018/12/09/alphafold-casp13-what-just-happened/ https://moalquraishi.wordpress.com/2018/12/09/alphafold-casp...)