用3D坐标和电子特征联合预测反应产率，能自动识别空间阻碍。
ChemFusion: A Multimodal Cross-Attention Network for Reaction Yield Prediction
- 融合电子特征与3D原子坐标，通过交叉注意力动态建模
- 在多种偶联反应上表现超越传统单一模型，预测更准
- 可自学习识别空间位阻，结果有物理可解释性
过渡金属催化的反应结果预测因物理与化学变量的复杂交互而极具挑战。计算瓶颈在于如何有效融合广泛的电子描述符与反应位点局部的三维几何结构。为弥合这一表征鸿沟，我们提出ChemFusion——一种混合神经网络，将传统电子特征与显式3D原子坐标相融合。该模型采用交叉注意力机制，使全局电子态能够动态关注未池化的分子点云中的特定空间约束。在多样化的交叉偶联反应数据集上进行基准测试表明，该方法显著优于传统单模态框架，预测性能优异。更重要的是，提取注意力矩阵显示，该架构能自主学习识别并惩罚受限的空间位阻，实现具有物理依据的可解释性，证明空间感知网络可捕捉标准统计模型常忽略的复杂立体效应。
原文摘要 · Abstract (English)
Forecasting the outcomes of transition-metal-catalyzed reactions is notoriously complex due to the interplay of diverse physical and chemical variables. A persistent computational bottleneck has been effectively merging broad electronic descriptors with the localized, three-dimensional geometry of the reactive site. To bridge this representation gap, we present ChemFusion, a hybrid neural network that fuses conventional electronic features with explicit 3D atomic coordinates. Using a cross-attention mechanism, the model enables global electronic states to dynamically attend to specific spatial constraints within un-pooled molecular point clouds. When benchmarked against a diverse library of cross-couplings, this approach delivers exceptional predictive performance, decisively surpassing traditional single-modality frameworks. Importantly, extracting the attention matrices reveals that the architecture autonomously learns to identify and penalize restrictive steric hindrances. This provides a physically grounded interpretability, demonstrating that spatially aware networks can navigate complex reaction sterics that standard statistical models typically miss.
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