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

Understand

Finding the main product of a chemical reaction is one of the important problems of organic chemistry.

• This paper describes a method of applying a neural machine translation model to the prediction of organic chemical reactions.
• In order to translate 'reactants and reagents' to 'products', a gated recurrent unit based sequence-to-sequence model and a parser to generate input tokens for model from reaction SMILES strings were built.
• Training sets are composed of reactions from the patent databases, and reactions manually generated applying the elementary reactions in an organic chemistry textbook of Wade.