  Deep generative models have emerged as a powerful tool for learning useful
molecular representations and designing novel molecules with desired
properties, with applications in drug discovery and material design. However,
most existing deep generative models are restricted due to lack of spatial
information. Here we propose augmentation of deep generative models with
topological data analysis (TDA) representations, known as persistence images,
for robust encoding of 3D molecular geometry. We show that the TDA augmentation
of a character-based Variational Auto-Encoder (VAE) outperforms
state-of-the-art generative neural nets in accurately modeling the structural
composition of the QM9 benchmark. Generated molecules are valid, novel, and
diverse, while exhibiting distinct electronic property distribution, namely
higher sample population with small HOMO-LUMO gap. These results demonstrate
that TDA features indeed provide crucial geometric signal for learning abstract
structures, which is non-trivial for existing generative models operating on
string, graph, or 3D point sets to capture.
