用通用模板+数据增强，提升化学反应产物预测准确率。
A Transformer Model for Predicting Chemical Products from Generic SMARTS Templates with Data Augmentation
- 基于SMARTS通用模板构建反应预测框架
- 模型在未见反应上达到高准确率
- 首个支持SMARTS模板的数据增强方法
准确预测化学反应结果是计算化学中的重大挑战。现有模型依赖高度特定的反应模板或无模板方法，均存在局限。本文提出包含20个通用反应模板的广义反应集（BRS），采用SMARTS表示法描述子结构与反应性。同时，提出ProPreT5——首个可直接处理并应用SMARTS模板的T5类化学语言模型。为提升泛化能力，首次设计了针对SMARTS的增强策略，在模式层面引入结构多样性。在增强模板训练下，ProPreT5展现出优异的预测性能和对未见反应的泛化能力。该工作为模板化反应预测提供了新颖且实用的解决方案，推动该领域发展。
原文摘要 · Abstract (English)
The accurate prediction of chemical reaction outcomes is a major challenge in computational chemistry. Current models rely heavily on either highly specific reaction templates or template-free methods, both of which present limitations. To address these, this work proposes the Broad Reaction Set (BRS), a set featuring 20 generic reaction templates written in SMARTS, a pattern-based notation designed to describe substructures and reactivity. Additionally, we introduce ProPreT5, a T5-based model specifically adapted for chemistry and, to the best of our knowledge, the first language model capable of directly handling and applying SMARTS reaction templates. To further improve generalization, we propose the first augmentation strategy for SMARTS, which injects structural diversity at the pattern level. Trained on augmented templates, ProPreT5 demonstrates strong predictive performance and generalization to unseen reactions. Together, these contributions provide a novel and practical alternative to current methods, advancing the field of template-based reaction prediction.
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