A method for predicting molecular point group based on graph neural networks

Molecular symmetry is fundamental to understanding molecular properties, designing functional materials, and optimizing chemical structures. Traditional symmetry determination methods, based on mathematical and rule-based approaches, are often limited by high computational cost and low efficiency. At present, deep learning methods predicting molecular 3D conformations from 2D structures also neglect molecular symmetry and point group considerations. To address these challenges, we propose a novel task: predicting the point group of a molecule's most stable 3D conformation using only its 2D topological graph, thereby enabling symmetry-aware conformation prediction. We adopt Graph Neural Networks (GNNs) to learn from molecular graph structures, and evaluate several GNN variants on this task. Among them, the Graph Isomorphism Network (GIN) achieves the highest accuracy by effectively capturing both local connectivity and global structural information. Experiments on the QM9 dataset show that our method achieves 92.7% accuracy and an F1-score of 0.924 on the test set, significantly surpassing both traditional approaches and other GNN-based methods. This work demonstrates the potential of deep learning in automated, efficient, and accurate molecular symmetry prediction, providing a valuable tool for future research in computational chemistry and material science.