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Issue 81, 2019

Molecular Transformer unifies reaction prediction and retrosynthesis across pharma chemical space

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Abstract

Predicting how a complex molecule reacts with different reagents, and how to synthesise complex molecules from simpler starting materials, are fundamental to organic chemistry. We show that an attention-based machine translation model – Molecular Transformer – tackles both reaction prediction and retrosynthesis by learning from the same dataset. Reagents, reactants and products are represented as SMILES text strings. For reaction prediction, the model “translates” the SMILES of reactants and reagents to product SMILES, and the converse for retrosynthesis. Moreover, a model trained on publicly available data is able to make accurate predictions on proprietary molecules extracted from pharma electronic lab notebooks, demonstrating generalisability across chemical space. We expect our versatile framework to be broadly applicable to problems such as reaction condition prediction, reagent prediction and yield prediction.

Graphical abstract: Molecular Transformer unifies reaction prediction and retrosynthesis across pharma chemical space

Article information


Submitted
04 Jul 2019
Accepted
29 Aug 2019
First published
29 Aug 2019

Chem. Commun., 2019,55, 12152-12155
Article type
Communication

Molecular Transformer unifies reaction prediction and retrosynthesis across pharma chemical space

A. A. Lee, Q. Yang, V. Sresht, P. Bolgar, X. Hou, J. L. Klug-McLeod and C. R. Butler, Chem. Commun., 2019, 55, 12152 DOI: 10.1039/C9CC05122H

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