  Discovery of atomistic systems with desirable properties is a major challenge
in chemistry and material science. Here we introduce a novel, autoregressive,
convolutional deep neural network architecture that generates molecular
equilibrium structures by sequentially placing atoms in three-dimensional
space. The model estimates the joint probability over molecular configurations
with tractable conditional probabilities which only depend on distances between
atoms and their nuclear charges. It combines concepts from state-of-the-art
atomistic neural networks with auto-regressive generative models for images and
speech. We demonstrate that the architecture is capable of generating molecules
close to equilibrium for constitutional isomers of C$_7$O$_2$H$_{10}$.
