通过显式建模化学键，提升分子性质预测精度
Enhancing Molecular Property Predictions by Learning from Bond Modelling and Interactions
- 构建原子与键双通道并行的图神经网络框架
- 在多个数据集上达到新最优性能，最高提升4.2%
- 适合需要高精度分子性质预测的研究者使用
分子表示学习对理解与预测分子性质至关重要。然而，传统以原子为中心的模型仅将化学键视为成对相互作用，常忽略共轭、立体选择性等复杂的键级现象，限制了对细微化学行为的预测能力。为此，我们提出 extbf{DeMol}，一种受信息论分析启发的双图框架，其架构基于键中心视角的信息增益。DeMol 通过并行的原子中心与键中心通道显式建模分子，并利用多尺度双螺旋块协同学习原子-原子、原子-键和键-键间的复杂交互。通过基于共价半径的正则化项增强几何一致性，确保化学结构合理性。在 PCQM4Mv2、OC20 IS2RE、QM9 及 MoleculeNet 等多样化基准上的全面评估表明，DeMol 达到新最优水平，显著优于现有方法。结果证实显式建模键信息及其相互作用的优势，为更鲁棒、精准的分子机器学习开辟道路。
原文摘要 · Abstract (English)
Molecule representation learning is crucial for understanding and predicting molecular properties. However, conventional atom-centric models, which treat chemical bonds merely as pairwise interactions, often overlook complex bond-level phenomena like resonance and stereoselectivity. This oversight limits their predictive accuracy for nuanced chemical behaviors. To address this limitation, we introduce \textbf{DeMol}, a dual-graph framework whose architecture is motivated by a rigorous information-theoretic analysis demonstrating the information gain from a bond-centric perspective. DeMol explicitly models molecules through parallel atom-centric and bond-centric channels. These are synergistically fused by multi-scale Double-Helix Blocks designed to learn intricate atom-atom, atom-bond, and bond-bond interactions. The framework's geometric consistency is further enhanced by a regularization term based on covalent radii to enforce chemically plausible structures. Comprehensive evaluations on diverse benchmarks, including PCQM4Mv2, OC20 IS2RE, QM9, and MoleculeNet, show that DeMol establishes a new state-of-the-art, outperforming existing methods. These results confirm the superiority of explicitly modelling bond information and interactions, paving the way for more robust and accurate molecular machine learning.
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