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I disagree on protein folding- It seems to me that AlphaGo Zero is a great analogy to the protein folding problem: If you have a good algorithm for folding 100 amino-acid proteins you can use that to brute force an algorithm for 110 amino-acid proteins, then use that as a training set to develop a good algorithm for 110 amino-acid proteins, then continue iterating in this way (or so I hope)



I think AlphaGo is a fundamentally different problem than is the protein folding problem. I can generate a Go board and a sequence of strictly valid moves because I know the rules for Go. With certain limited high level exceptions (like the sequence is linear, you can't have bond angles of a certain degree etc.) those rules don't exist as such for protein folding. In other words if I generate 1000 protein folds, I don't know which ones are physically valid. That's a problem for the kind of iteration I think you're describing, though I may be misunderstanding.

Another way of seeing this limitation is that we have great models for predicting what the weather will be like a few days from now, almost down the hour. But when asked to predict whether it will snow a month from now, we can't. That's because highly accurate models on small scale timescales become too noisy on longer timescales. In protein folding, just s/timescale/sequence length/.


Well they are giving it a try:

>DeepMind has already begun using AlphaGo Zero to study protein folding and has promised it will soon publish new findings.

http://www.telegraph.co.uk/science/2017/10/18/alphago-zero-g...


Ha! Apparently they had the same thought :)




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