  The rational design of molecules with desired properties is a long-standing
challenge in chemistry. Generative neural networks have emerged as a powerful
approach to sample novel molecules from a learned distribution. Here, we
propose a conditional generative neural network for 3d molecular structures
with specified chemical and structural properties. This approach is agnostic to
chemical bonding and enables targeted sampling of novel molecules from
conditional distributions, even in domains where reference calculations are
sparse. We demonstrate the utility of our method for inverse design by
generating molecules with specified motifs or composition, discovering
particularly stable molecules, and jointly targeting multiple electronic
properties beyond the training regime.
