用预训练模型学习化学键变化模式，生成可解释的反应描述符
A Pre-trained Reaction Embedding Descriptor Capturing Bond Transformation Patterns
- 基于预训练模型学习真实反应中的键形成与断裂模式
- 在USPTO-50k数据集上实现更贴近键变化相似性的聚类
- 能可视化反应空间多样性，适合化学反应分析与发现
随着数据驱动的反应预测模型兴起，有效的反应描述符对于弥合现实化学与数字表示之间的差距至关重要。然而，通用的、面向反应级别的描述符仍然稀缺。本研究提出RXNEmb，一种源自RXNGraphormer的新型反应级描述符，该模型通过预训练区分真实反应与存在错误键变化的虚构反应，从而学习内在的键形成与断裂模式。我们通过数据驱动的方式对USPTO-50k数据集进行重新聚类，得到的分类比基于规则的类别更直接反映键变化的相似性。结合降维技术，RXNEmb实现了反应空间多样性的可视化。此外，注意力权重分析揭示了模型对化学关键位点的关注，提供机制洞察。RXNEmb是一种强大且可解释的反应指纹工具，为反应分析与发现中的数据驱动方法铺平道路。
原文摘要 · Abstract (English)
With the rise of data-driven reaction prediction models, effective reaction descriptors are crucial for bridging the gap between real-world chemistry and digital representations. However, general-purpose, reaction-wise descriptors remain scarce. This study introduces RXNEmb, a novel reaction-level descriptor derived from RXNGraphormer, a model pre-trained to distinguish real reactions from fictitious ones with erroneous bond changes, thereby learning intrinsic bond formation and cleavage patterns. We demonstrate its utility by data-driven re-clustering of the USPTO-50k dataset, yielding a classification that more directly reflects bond-change similarities than rule-based categories. Combined with dimensionality reduction, RXNEmb enables visualization of reaction space diversity. Furthermore, attention weight analysis reveals the model's focus on chemically critical sites, providing mechanistic insight. RXNEmb serves as a powerful, interpretable tool for reaction fingerprinting and analysis, paving the way for more data-centric approaches in reaction analysis and discovery.
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