There is an intuitive analogy of an organic chemist's understanding of a
compound and a language speaker's understanding of a word. Consequently, it is
possible to introduce the basic concepts and analyze potential impacts of
linguistic analysis to the world of organic chemistry. In this work, we cast
the reaction prediction task as a translation problem by introducing a
template-free sequence-to-sequence model, trained end-to-end and fully
data-driven. We propose a novel way of tokenization, which is arbitrarily
extensible with reaction information. With this approach, we demonstrate
results superior to the state-of-the-art solution by a significant margin on
the top-1 accuracy. Specifically, our approach achieves an accuracy of 80.1%
without relying on auxiliary knowledge such as reaction templates. Also, 66.4%
accuracy is reached on a larger and noisier dataset.

该研究介绍了一种基于序列到序列模型的无模板反应预测方法，使用一种新型的可扩展标记方法，不涉及反应模板等辅助知识，达到了 80.1% 的准确率，其核心思想是运用语言学分析的思维方式来解决有机化学问题。