  Recent advancements in machine learning techniques for protein folding
motivate better results in its inverse problem -- protein design. In this work
we introduce a new graph mimetic neural network, MimNet, and show that it is
possible to build a reversible architecture that solves the structure and
design problems in tandem, allowing to improve protein design when the
structure is better estimated. We use the ProteinNet data set and show that the
state of the art results in protein design can be improved, given recent
architectures for protein folding.
