Substructure-based Neural Machine Translation for Retrosynthetic Prediction

This work presents a new template-free neural machine translation method for retrosynthetic reaction prediction by learning the chemical change at a substructural level. The proposed method effectively solves all the translation issues arising from SMILES-based representation of molecular structures.