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A new web-based app to help you predict chemical reactions. 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 80.3% without relying on auxiliary knowledge such as reaction templates. Also, 65.4% accuracy is reached on a larger and noisier dataset.

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