  Variational autoencoder (VAE) is a popular method for drug discovery and
there had been a great deal of architectures and pipelines proposed to improve
its performance. But the VAE model itself suffers from deficiencies such as
poor manifold recovery when data lie on low-dimensional manifold embedded in
higher dimensional ambient space and they manifest themselves in each
applications differently. The consequences of it in drug discovery is somewhat
under-explored. In this paper, we study how to improve the similarity of the
data generated via VAE and the training dataset by improving manifold recovery
via a 2-stage VAE where the second stage VAE is trained on the latent space of
the first one. We experimentally evaluated our approach using the ChEMBL
dataset as well as a polymer datasets. In both dataset, the 2-stage VAE method
is able to improve the property statistics significantly from a pre-existing
method.
