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Substructure-based Neural Machine Translation for Retrosynthetic Prediction

preprint
revised on 31.07.2020 and posted on 31.07.2020 by Umit Ucak, Taek Kang, Junsu Ko, Juyong Lee

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.

History

Email Address of Submitting Author

umit@kangwon.ac.kr

Institution

Kangwon National University

Country

Republic of Korea

ORCID For Submitting Author

0000-0002-9088-0915

Declaration of Conflict of Interest

no conflict of interest

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