  In this work we introduce an Autoencoder for molecular conformations. Our
proposed model converts the discrete spatial arrangements of atoms in a given
molecular graph (conformation) into and from a continuous fixed-sized latent
representation. We demonstrate that in this latent representation, similar
conformations cluster together while distinct conformations split apart.
Moreover, by training a probabilistic model on a large dataset of molecular
conformations, we demonstrate how our model can be used to generate diverse
sets of energetically favorable conformations for a given molecule. Finally, we
show that the continuous representation allows us to utilize optimization
methods to find molecules that have conformations with favourable spatial
properties.
