exactly which deep learning technique do you propose unleashing on protein folding?  Deep learning is not &#x27;magic&#x27;, it is a set of tools good at a certain subset of machine learning tasks, and the impressive thing is how many machine learning tasks can be shoehorned and contorted into the deep learning rubric, i.e. &#x27;fuzzy classification via scoring&#x27;.  It should be obvious how image recognition and voice recognition fit this rubric.<p>&quot;Playing Go&quot;, which does not fit this rubric obviously, I think, is conquered by deep learning because there is a discrete matrix of &#x27;moves&#x27; which looks awfully lot like an image recognition matrix, and so a move which optimizes score (&#x27;classification&#x27;) can be selected.<p>Fundamentally one of the problems is that we don&#x27;t know how to score proteins.  We also don&#x27;t necessarily know how to score go, but it&#x27;s trivial to generate solutions by running hypothetical matches because the game outcome is deterministic, solvable, and definite.  While there is a decent subset of proteins whose known structures could be used to seed a database for scoring, it is very small in comparison to the universe of possibilities (20^n, where n is the length of the amino acid chain).<p>In short:  Given an arbitrary &quot;end-board layout&quot; I can with our existing human knowledgebase tell you which go player has won.  Given an arbitrary amino acid sequence and a proposed structure, I cannot with our existing human knowledgebase tell you if the structure is correct.  Because of this limitation in creating a scoring system, while I won&#x27;t say that deep learning won&#x27;t be used to improve protein folding (maybe it will, indirectly) it almost certainly won&#x27;t be used <i>directly</i>, because it is not in the same class of problem.