Substructure-based Neural Machine Translation for Retrosynthetic Prediction

31 July 2020, Version 2
This content is a preprint and has not undergone peer review at the time of posting.

Abstract

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.

Keywords

Neural Machine Translation
Sequence-to-Sequence Models
retrosynthesis prediction

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