Tiered Latent Representations and Latent Spaces for Molecular Graphs
This addresses the challenge of representing and utilizing groups in molecular graphs for applications in chemistry and related fields, though it appears incremental as it builds on existing graph autoencoders and neural networks.
The paper tackles the problem of representing identifiable subgraphs (groups) in molecular graphs, which flat or fully hierarchical latent representations fail to handle effectively, by proposing tiered latent representations and latent spaces that explicitly model atoms, groups, and molecules, resulting in a simpler and more interpretable approach.
Molecular graphs generally contain subgraphs (known as groups) that are identifiable and significant in composition, functionality, geometry, etc. Flat latent representations (node embeddings or graph embeddings) fail to represent, and support the use of, groups. Fully hierarchical latent representations, on the other hand, are difficult to learn and, even if learned, may be too complex to use or interpret. We propose tiered latent representations and latent spaces for molecular graphs as a simple way to explicitly represent and utilize groups, which consist of the atom (node) tier, the group tier and the molecule (graph) tier. Specifically, we propose an architecture for learning tiered latent representations and latent spaces using graph autoencoders, graph neural networks, differentiable group pooling and the membership matrix. We discuss its various components, major challenges and related work, for both a deterministic and a probabilistic model. We also briefly discuss the usage and exploration of tiered latent spaces. The tiered approach is applicable to other types of structured graphs similar in nature to molecular graphs.