  Learning expressive molecular representations is crucial to facilitate the
accurate prediction of molecular properties. Despite the significant
advancement of graph neural networks (GNNs) in molecular representation
learning, they generally face limitations such as neighbors-explosion,
under-reaching, over-smoothing, and over-squashing. Also, GNNs usually have
high computational costs because of the large-scale number of parameters.
Typically, such limitations emerge or increase when facing relatively
large-size graphs or using a deeper GNN model architecture. An idea to overcome
these problems is to simplify a molecular graph into a small, rich, and
informative one, which is more efficient and less challenging to train GNNs. To
this end, we propose a novel molecular graph coarsening framework named FunQG
utilizing Functional groups, as influential building blocks of a molecule to
determine its properties, based on a graph-theoretic concept called Quotient
Graph. By experiments, we show that the resulting informative graphs are much
smaller than the molecular graphs and thus are good candidates for training
GNNs. We apply the FunQG on popular molecular property prediction benchmarks
and then compare the performance of some popular baseline GNNs on the obtained
datasets with the performance of several state-of-the-art baselines on the
original datasets. By experiments, this method significantly outperforms
previous baselines on various datasets, besides its dramatic reduction in the
number of parameters and low computational costs. Therefore, the FunQG can be
used as a simple, cost-effective, and robust method for solving the molecular
representation learning problem.
