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Finding the main product of a chemical reaction is one of the importantproblems of organic chemistry. This paper describes a method of applying aneural machine translation model to the prediction of organic chemicalreactions. In order to translate 'reactants and reagents' to 'products', agated recurrent unit based sequence-to-sequence model and a parser to generateinput tokens for model from reaction SMILES strings were built. Training setsare composed of reactions from the patent databases, and reactions manuallygenerated applying the elementary reactions in an organic chemistry textbook ofWade. The trained models were tested by examples and problems in the textbook.The prediction process does not need manual encoding of rules (e.g., SMARTStransformations) to predict products, hence it only needs sufficient trainingreaction sets to learn new types of reactions.

Linking the Neural Machine Translation and the Prediction of Organic Chemistry Reactions