Title: "Found in Translation": Predicting Outcomes of Complex Organic Chemistry Reactions using Neural Sequence-to-Sequence Models,
Abstract: 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.