  Machine learning in drug discovery has been focused on virtual screening of
molecular libraries using discriminative models. Generative models are an
entirely different approach that learn to represent and optimize molecules in a
continuous latent space. These methods have been increasingly successful at
generating two dimensional molecules as SMILES strings and molecular graphs. In
this work, we describe deep generative models of three dimensional molecular
structures using atomic density grids and a novel fitting algorithm for
converting continuous grids to discrete molecular structures. Our models
jointly represent drug-like molecules and their conformations in a latent space
that can be explored through interpolation. We are also able to sample diverse
sets of molecules based on a given input compound and increase the probability
of creating valid, drug-like molecules.
