To accurately predict molecular properties, it is important to learn expressive molecular representations. Graph neural networks (GNNs) have made significant advances in this area, but they often
face limitations like neighbors-explosion, under-reaching, over-smoothing, and over-squashing. Additionally, GNNs tend to have high computational costs due to their large number of parameters.
These limitations emerge or increase when dealing with larger graphs or deeper GNN models. One
potential solution is to simplify the molecular graph into a smaller, richer, and more informative one
that is easier to train GNNs. Our proposed molecular graph coarsening framework called FunQG,
uses Functional groups as building blocks to determine a molecule’s properties, based on a graph-
theoretic concept called Quotient Graph. We show through experiments that the resulting informative
graphs are much smaller than the original molecular graphs and are thus more suitable for training
GNNs. We apply FunQG to popular molecular property prediction benchmarks and compare the
performance of popular baseline GNNs on the resulting datasets to that of state-of-the-art baselines
on the original datasets. Our experiments demonstrate that FunQG yields notable results on various
datasets while dramatically reducing the number of parameters and computational costs. By utilizing
functional groups, we can achieve an interpretable framework that indicates their significant role in
determining the properties of molecular quotient graphs. Consequently, FunQG is a straightforward,
computationally efficient, and generalizable solution for addressing the molecular representation
learning problem.