  Variational autoencoder (VAE) is a popular method for drug discovery and
various architectures and pipelines have been proposed to improve its
performance. However, VAE approaches are known to suffer from poor manifold
recovery when the data lie on a low-dimensional manifold embedded in a higher
dimensional ambient space [Dai and Wipf, 2019]. The consequences of it in drug
discovery are somewhat under-explored. In this paper, we explore applying a
multi-stage VAE approach, that can improve manifold recovery on a synthetic
dataset, to the field of drug discovery. We experimentally evaluate our
multi-stage VAE approach using the ChEMBL dataset and demonstrate its ability
to improve the property statistics of generated molecules substantially from
pre-existing methods without incorporating property predictors into the
training pipeline. We further fine-tune our models on two curated and much
smaller molecule datasets that target different proteins. Our experiments show
an increase in the number of active molecules generated by the multi-stage VAE
in comparison to their one-stage equivalent. For each of the two tasks, our
baselines include methods that use learned property predictors to incorporate
target metrics directly into the training objective and we discuss
complications that arise with this methodology.
