  Tiered latent representations and latent spaces for molecular graphs provide
a simple but effective way to explicitly represent and utilize groups (e.g.,
functional groups), which consist of the atom (node) tier, the group tier and
the molecule (graph) tier. They can be learned using the tiered graph
autoencoder architecture. In this paper we discuss adapting tiered graph
autoencoders for use with PyTorch Geometric, for both the deterministic tiered
graph autoencoder model and the probabilistic tiered variational graph
autoencoder model. We also discuss molecular structure information sources that
can be accessed to extract training data for molecular graphs. To support
transfer learning, a critical consideration is that the information must
utilize standard unique molecule and constituent atom identifiers. As a result
of using tiered graph autoencoders for deep learning, each molecular graph
possesses tiered latent representations. At each tier, the latent
representation consists of: node features, edge indices, edge features,
membership matrix, and node embeddings. This enables the utilization and
exploration of tiered molecular latent spaces, either individually (the node
tier, the group tier, or the graph tier) or jointly, as well as navigation
across the tiers.
