  Tiered graph autoencoders provide the architecture and mechanisms for
learning tiered latent representations and latent spaces for molecular graphs
that explicitly represent and utilize groups (e.g., functional groups). This
enables the utilization and exploration of tiered molecular latent spaces,
either individually - the node (atom) tier, the group tier, or the graph
(molecule) tier - or jointly, as well as navigation across the tiers. In this
paper, we discuss the use of tiered graph autoencoders together with graph
prediction for molecular graphs. We show features of molecular graphs used, and
groups in molecular graphs identified for some sample molecules. We briefly
review graph prediction and the QM9 dataset for background information, and
discuss the use of tiered graph embeddings for graph prediction, particularly
weighted group pooling. We find that functional groups and ring groups
effectively capture and represent the chemical essence of molecular graphs
(structures). Further, tiered graph autoencoders and graph prediction together
provide effective, efficient and interpretable deep learning for molecular
graphs, with the former providing unsupervised, transferable learning and the
latter providing supervised, task-optimized learning.

Machine Learning;Biomolecules