State-of-the-art augmented NLP transformer models for direct and single-step retrosynthesis

Development of algorithms to predict reactant and reagents given a target molecule is key to accelerate retrosynthesis approaches. Here the authors demonstrate that applying augmentation techniques to the SMILE representation of target data significantly improves the quality of the reaction predicti...

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Autores principales:	Igor V. Tetko, Pavel Karpov, Ruud Van Deursen, Guillaume Godin
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/f3db04ada827413392a0875994a0169d
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spelling	oai:doaj.org-article:f3db04ada827413392a0875994a0169d2021-12-02T17:32:14ZState-of-the-art augmented NLP transformer models for direct and single-step retrosynthesis10.1038/s41467-020-19266-y2041-1723https://doaj.org/article/f3db04ada827413392a0875994a0169d2020-11-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-19266-yhttps://doaj.org/toc/2041-1723Development of algorithms to predict reactant and reagents given a target molecule is key to accelerate retrosynthesis approaches. Here the authors demonstrate that applying augmentation techniques to the SMILE representation of target data significantly improves the quality of the reaction predictions.Igor V. TetkoPavel KarpovRuud Van DeursenGuillaume GodinNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-11 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Igor V. Tetko Pavel Karpov Ruud Van Deursen Guillaume Godin State-of-the-art augmented NLP transformer models for direct and single-step retrosynthesis
description	Development of algorithms to predict reactant and reagents given a target molecule is key to accelerate retrosynthesis approaches. Here the authors demonstrate that applying augmentation techniques to the SMILE representation of target data significantly improves the quality of the reaction predictions.
format	article
author	Igor V. Tetko Pavel Karpov Ruud Van Deursen Guillaume Godin
author_facet	Igor V. Tetko Pavel Karpov Ruud Van Deursen Guillaume Godin
author_sort	Igor V. Tetko
title	State-of-the-art augmented NLP transformer models for direct and single-step retrosynthesis
title_short	State-of-the-art augmented NLP transformer models for direct and single-step retrosynthesis
title_full	State-of-the-art augmented NLP transformer models for direct and single-step retrosynthesis
title_fullStr	State-of-the-art augmented NLP transformer models for direct and single-step retrosynthesis
title_full_unstemmed	State-of-the-art augmented NLP transformer models for direct and single-step retrosynthesis
title_sort	state-of-the-art augmented nlp transformer models for direct and single-step retrosynthesis
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/f3db04ada827413392a0875994a0169d
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State-of-the-art augmented NLP transformer models for direct and single-step retrosynthesis

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