Multi-Level Fusion Graph Neural Network for Molecule Property Prediction

• URL: http://arxiv.org/abs/2507.03430v1
• Date: Fri, 04 Jul 2025 09:38:19 GMT
• Title: Multi-Level Fusion Graph Neural Network for Molecule Property Prediction
• Authors: XiaYu Liu, Hou-biao Li, Yang Liu, Chao Fan,
• Abstract summary: We propose a Multi-Level Fusion Graph Neural Network (MLFGNN) that integrates Graph Attention Networks and a novel Graph Transformer.<n>Experiments on multiple benchmark datasets demonstrate that MLFGNN consistently outperforms state-of-the-art methods in both classification and regression tasks.
• Score: 7.496721948662087
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate molecular property prediction is essential in drug discovery and related fields. However, existing graph neural networks (GNNs) often struggle to simultaneously capture both local and global molecular structures. In this work, we propose a Multi-Level Fusion Graph Neural Network (MLFGNN) that integrates Graph Attention Networks and a novel Graph Transformer to jointly model local and global dependencies. In addition, we incorporate molecular fingerprints as a complementary modality and introduce a mechanism of interaction between attention to adaptively fuse information across representations. Extensive experiments on multiple benchmark datasets demonstrate that MLFGNN consistently outperforms state-of-the-art methods in both classification and regression tasks. Interpretability analysis further reveals that the model effectively captures task-relevant chemical patterns, supporting the usefulness of multi-level and multi-modal fusion in molecular representation learning.

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