  We devise an approach for targeted molecular design, a problem of interest in
computational drug discovery: given a target protein site, we wish to generate
a chemical with both high binding affinity to the target and satisfactory
pharmacological properties. This problem is made difficult by the enormity and
discreteness of the space of potential therapeutics, as well as the
graph-structured nature of biomolecular surface sites. Using a dataset of
protein-ligand complexes, we surmount these issues by extracting a signature of
the target site with a graph convolutional network and by encoding the discrete
chemical into a continuous latent vector space. The latter embedding permits
gradient-based optimization in molecular space, which we perform using learned
differentiable models of binding affinity and other pharmacological properties.
We show that our approach is able to efficiently optimize these multiple
objectives and discover new molecules with potentially useful binding
properties, validated via docking methods.
