针对分子属性预测中子结构差异被忽视的问题，提出自适应专家模型提升准确率与可解释性。
Adaptive Substructure-Aware Expert Model for Molecular Property Prediction
- 采用专家混合架构，动态识别分子中的正负子结构
- 在8个基准数据集上表现优于现有方法，提升准确率与可解释性
- 适合药物发现和毒性评估等需要精准子结构分析的场景
分子属性预测在药物发现和毒性评估中至关重要。尽管图神经网络（GNN）通过将分子建模为分子图取得了良好效果，但其依赖数据驱动学习，导致泛化能力受限，尤其在数据不平衡和多样子结构环境下表现不佳。现有方法常忽略不同子结构对分子属性的差异化贡献，一概而论。为此，我们提出ASE-Mol，一种基于GNN的新型框架，利用专家混合（MoE）机制实现分子属性预测。该方法结合BRICS分解与显著子结构感知，动态识别正向与负向子结构。通过引入MoE架构，有效降低负向基团的干扰，增强对正向基团的适应性。在8个基准数据集上的实验表明，ASE-Mol达到当前最优性能，显著提升了准确率与可解释性。
原文摘要 · Abstract (English)
Molecular property prediction is essential for applications such as drug discovery and toxicity assessment. While Graph Neural Networks (GNNs) have shown promising results by modeling molecules as molecular graphs, their reliance on data-driven learning limits their ability to generalize, particularly in the presence of data imbalance and diverse molecular substructures. Existing methods often overlook the varying contributions of different substructures to molecular properties, treating them uniformly. To address these challenges, we propose ASE-Mol, a novel GNN-based framework that leverages a Mixture-of-Experts (MoE) approach for molecular property prediction. ASE-Mol incorporates BRICS decomposition and significant substructure awareness to dynamically identify positive and negative substructures. By integrating a MoE architecture, it reduces the adverse impact of negative motifs while improving adaptability to positive motifs. Experimental results on eight benchmark datasets demonstrate that ASE-Mol achieves state-of-the-art performance, with significant improvements in both accuracy and interpretability.
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