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Consistency-regularized graph neural networks for molecular property prediction.

Jongmin Han¹, Seokho Kang¹

¹Department of Industrial Engineering, Sungkyunkwan University, 2066 Seobu-ro Jangan-gu, Suwon, 16419, Republic of Korea.

Neural Networks : the Official Journal of the International Neural Network Society

|October 7, 2025

Summary

This study introduces a consistency-regularized graph neural network (CRGNN) to improve molecular property prediction on small datasets. The method enhances graph neural network (GNN) performance by ensuring augmented molecular graph views align, overcoming limitations of traditional data augmentation.

Keywords:

Consistency regularization Data augmentation Graph neural networks Molecular property prediction

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Area of Science:

Computational Chemistry
Machine Learning
Cheminformatics

Background:

Graph neural networks (GNNs) excel at molecular property prediction but struggle with small datasets.
Standard data augmentation techniques often fail for molecular graphs, potentially altering inherent properties.

Purpose of the Study:

To develop a novel method, consistency-regularized graph neural network (CRGNN), for effective molecular graph augmentation in GNN training.
To enhance GNN performance on small molecular datasets by leveraging augmented graph representations.

Main Methods:

Applied molecular graph augmentation to generate strongly and weakly augmented views of each molecular graph.
Introduced a consistency regularization loss to encourage GNNs to map augmented views of the same graph closely in the representation space.
Integrated this loss into the GNN learning objective.

Main Results:

The CRGNN method effectively utilizes molecular graph augmentation, improving prediction performance.
Demonstrated superior performance compared to existing methods on various molecular benchmark datasets.
Performance gains were particularly significant on smaller training datasets.

Conclusions:

Consistency regularization offers a viable strategy to mitigate the negative effects of molecular graph augmentation in GNNs.
The proposed CRGNN method enhances the utility of data augmentation for molecular property prediction, especially in low-data regimes.
This approach advances the application of GNNs in cheminformatics for small molecule datasets.