End-to-end differentiable learning of protein structure (opens in new tab)

That doesn't make any sense unless I'm missing something, A0 is suited for a completely different problem than protein folding...

FWIW, AlphaGo like algorithms have already been applied to this domain, see AlphaChem

I do think however that protein folding is very much understudied in the ML community, relative to say the big three of vision, NLP, and speech. The lack of standardized data sets and benchmarks, not to mention the need for domain knowledge, have made it difficult to get into the field

Thanks for the answer! I hope then to see you in CASP (and CAMEO too, it is a great tool to test/refine your method). I was discussing a paper with a co-worker of mine (we also work on psp, we work on RBO Aleph). We had a hard time pinpointing the thing that made your method finally work. You have mentioned in your blog post that you have been working on it for years now, and I guess a lot of other people had the idea of using deep learning for psp. But what was the insight that made it all work, using LSTM? or was it many small refinements and hacks?

Oops I seemed to have skimmed it a bit too quickly. Thank you for the kind reply.

Yes! And I'd also add that there are others that come into play...

* Elucidating function by identifying similarity to other known structures

* Finding novel signaling mechanisms (see work on PHinder)

* Modeling co-receptor/ligand dynamics

* Identifying function of orphan receptors

* Working with ancestral genes by identifying descendant structure

* Classifying and clustering proteins based on solved structure

* Learning new biochemical mechanisms through active vs inactive state structures

...