  Embedding computation in molecular contexts incompatible with traditional
electronics is expected to have wide ranging impact in synthetic biology,
medicine, nanofabrication and other fields. A key remaining challenge lies in
developing programming paradigms for molecular computation that are
well-aligned with the underlying chemical hardware and do not attempt to
shoehorn ill-fitting electronics paradigms. We discover a surprisingly tight
connection between a popular class of neural networks (binary-weight ReLU aka
BinaryConnect) and a class of coupled chemical reactions that are absolutely
robust to reaction rates. The robustness of rate-independent chemical
computation makes it a promising target for bioengineering implementation. We
show how a BinaryConnect neural network trained in silico using well-founded
deep learning optimization techniques, can be compiled to an equivalent
chemical reaction network, providing a novel molecular programming paradigm. We
illustrate such translation on the paradigmatic IRIS and MNIST datasets. Toward
intended applications of chemical computation, we further use our method to
generate a chemical reaction network that can discriminate between different
virus types based on gene expression levels. Our work sets the stage for rich
knowledge transfer between neural network and molecular programming
communities.
