Accurate atom mapping, which establishes correspondences between atoms in reactants and products, is a crucial step in analyzing chemical reactions. In this paper, we present a novel end-to-end approach that formulates the atom mapping problem as a deep graph matching task. Our proposed model, AMNet (Atom Matching Network), utilizes molecular graph representations and employs various atom and bond features using graph neural networks to capture the intricate structural characteristics of molecules, ensuring precise atom correspondence predictions. Notably, AMNet incorporates the consideration of molecule symmetry, enhancing accuracy while simultaneously reducing computational complexity. The integration of the Weisfeiler-Lehman isomorphism test for symmetry identification refines the model’s predictions. Furthermore, our model maps the entire atom set in a chemical reaction, offering a comprehensive approach beyond focusing solely on the main molecules in reactions. We evaluated AMNet’s performance on a subset of USPTO reaction datasets, addressing various tasks, including assessing the impact of molecular symmetry identification, understanding the influence of feature selection on AMNet performance, and comparing its performance with the state-of-the-art method. The result reveals an average accuracy of 97.3% on mapped atoms, with 99.7% of reactions correctly mapped when the correct mapped atom is within the top 10 predicted atoms. Scientific contribution The paper introduces a novel end-to-end deep graph matching model for atom mapping, utilizing molecular graph representations to capture structural characteristics effectively. It enhances accuracy by integrating symmetry detection through the Weisfeiler-Lehman test, reducing the number of possible mappings and improving efficiency. Unlike previous methods, it maps the entire reaction, not just main components, providing a comprehensive view. Additionally, by integrating efficient graph matching techniques, it reduces computational complexity, making atom mapping more feasible.

中文翻译：

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通过深度图匹配学习化学反应中的对称感知原子映射

准确的原子映射可以建立反应物和产物中原子之间的对应关系，是分析化学反应的关键步骤。在本文中，我们提出了一种新颖的端到端方法，将原子映射问题表述为深度图匹配任务。我们提出的模型 AMNet（原子匹配网络）利用分子图表示，并利用图神经网络采用各种原子和键特征来捕获分子复杂的结构特征，确保精确的原子对应预测。值得注意的是，AMNet 考虑了分子对称性，提高了准确性，同时降低了计算复杂性。用于对称性识别的 Weisfeiler-Lehman 同构检验的集成完善了模型的预测。此外，我们的模型映射了化学反应中的整个原子集，提供了一种全面的方法，而不仅仅是只关注反应中的主要分子。我们评估了 AMNet 在 USPTO 反应数据集子集上的性能，解决了各种任务，包括评估分子对称识别的影响、了解特征选择对 AMNet 性能的影响，以及将其性能与最先进的方法进行比较。结果显示，映射原子的平均准确度为 97.3%，当正确映射的原子位于前 10 个预测原子内时，99.7% 的反应正确映射。科学贡献本文介绍了一种新颖的用于原子映射的端到端深度图匹配模型，利用分子图表示来有效捕获结构特征。它通过 Weisfeiler-Lehman 测试集成对称性检测来提高准确性，减少可能的映射数量并提高效率。与以前的方法不同，它绘制了整个反应，而不仅仅是主要成分，提供了全面的视图。此外，通过集成高效的图匹配技术，它降低了计算复杂性，使原子映射更加可行。