## Abstract

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.

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.

Original language | English |
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Pages (from-to) | 3275-3287 |

Number of pages | 13 |

Journal | Journal of chemical information and modeling |

Volume | 63.2023 |

Issue number | 11 |

DOIs | |

Publication status | Published - 15 May 2023 |