融合局部与全局结构的图神经网络，提升分子性质预测准确率
Multi-Level Fusion Graph Neural Network for Molecule Property Prediction
- 结合图注意力与新型图变换器，同时建模分子局部与全局特征
- 在多个基准数据集上超越现有方法，分类与回归任务均表现更优
- 支持可解释性分析，适合药物研发领域使用
准确预测分子性质对药物发现等领域至关重要。然而，现有图神经网络（GNN）难以同时捕捉分子的局部与全局结构。本文提出多层级融合图神经网络（MLFGNN），集成图注意力网络与一种新型图变换器，联合建模局部与全局依赖关系。此外，引入分子指纹作为互补模态，并设计注意力交互机制，实现不同表示间的信息自适应融合。在多个基准数据集上的大量实验表明，MLFGNN在分类与回归任务中均持续优于当前最优方法。可解释性分析显示，该模型能有效捕捉与任务相关的化学模式，验证了多层级、多模态融合在分子表征学习中的有效性。
原文摘要 · Abstract (English)
Accurate prediction of molecular properties 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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