5 ms·

I think the key to the success was translating folding into deep learning problem and solving it (maybe there is still something to win by optimizing the models

I think the key to the success was translating folding into deep learning problem and solving it (maybe there is still something to win by optimizing the models). The academia and industry were not treating folding as deep learning problem and they were using 'old' methods with slight improvements from year to year. The usage of deep learning was a breakthrough and will speed up research in this field.

throwawaylolx 8y ago
That's most definitely not the case: https://scholar.google.com/scholar?q=deep+learning+protein+folding https://scholar.google.com/scholar?q=deep+learning+protein+f...
pgeorgi 8y ago
The Go community was rather forthcoming to what AlphaGo brought, because it provided new insights to a field that some of them study quasi-religiously. Let's see how pharmaceutical companies are dealing with that, or the next fields DeepMind will enter and shake up like that.
sanxiyn 8y ago
Eh... no? If you look at abstracts, Zhang group (the second place) also used "deep-learning based contact predictor". Really, the usage of deep learning was kind of standard long before CASP13. If you read OP, AlphaFold's main innovation is predicting distance instead of contact (regression instead of binary classification), which was also independently developed by Xu, and further, using probability distribution over distance instead of simply choosing the best distance.
- HarryHirsch 8y ago
  Yes, you wonder how Alphafold compares with the combined wisdom of Foldit. You start out with a secondary structure prediction from Zhang lab and ask a panel of experienced humans to fold up the protein into a plausible tertiary structure. As a human you have a good idea what larger pieces fit where, but AI still has problems with the large picture, cf. the leopard sofa problem.

5 ms·

I think the key to the success was translating folding into deep learning problem and solving it (maybe there is still something to win by optimizing the models

by pplonski86 8y ago

throwawaylolx 8y ago
That's most definitely not the case: https://scholar.google.com/scholar?q=deep+learning+protein+folding https://scholar.google.com/scholar?q=deep+learning+protein+f...
pgeorgi 8y ago
The Go community was rather forthcoming to what AlphaGo brought, because it provided new insights to a field that some of them study quasi-religiously. Let's see how pharmaceutical companies are dealing with that, or the next fields DeepMind will enter and shake up like that.
sanxiyn 8y ago
Eh... no? If you look at abstracts, Zhang group (the second place) also used "deep-learning based contact predictor". Really, the usage of deep learning was kind of standard long before CASP13. If you read OP, AlphaFold's main innovation is predicting distance instead of contact (regression instead of binary classification), which was also independently developed by Xu, and further, using probability distribution over distance instead of simply choosing the best distance.
- HarryHirsch 8y ago
  Yes, you wonder how Alphafold compares with the combined wisdom of Foldit. You start out with a secondary structure prediction from Zhang lab and ask a panel of experienced humans to fold up the protein into a plausible tertiary structure. As a human you have a good idea what larger pieces fit where, but AI still has problems with the large picture, cf. the leopard sofa problem.