FunQG: Molecular Representation Learning Via Quotient Graphs

TL;DR

FunQG introduces a graph coarsening method based on functional groups to create smaller, informative molecular graphs, significantly improving GNN performance and efficiency in molecular property prediction tasks.

Contribution

This paper presents a novel coarsening framework using quotient graphs and functional groups, enhancing molecular representation learning by reducing graph size and computational costs.

Findings

Outperforms previous baselines on multiple molecular property datasets.

Reduces the number of parameters and computational costs dramatically.

Produces smaller, informative graphs suitable for efficient GNN training.

Abstract

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.