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

We investigated the effect of different training scenarios on predicting the (retro)synthesis of chemical compounds using text-like representation of chemical reactions (SMILES) and Natural Language Processing (NLP) neural network Transformer architecture. We showed that data augmentation, which is...

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Autores principales: Tetko, Igor V., Karpov, Pavel, Van Deursen, Ruud, Godin, Guillaume
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7643129/
https://www.ncbi.nlm.nih.gov/pubmed/33149154
http://dx.doi.org/10.1038/s41467-020-19266-y
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author Tetko, Igor V.
Karpov, Pavel
Van Deursen, Ruud
Godin, Guillaume
author_facet Tetko, Igor V.
Karpov, Pavel
Van Deursen, Ruud
Godin, Guillaume
author_sort Tetko, Igor V.
collection PubMed
description We investigated the effect of different training scenarios on predicting the (retro)synthesis of chemical compounds using text-like representation of chemical reactions (SMILES) and Natural Language Processing (NLP) neural network Transformer architecture. We showed that data augmentation, which is a powerful method used in image processing, eliminated the effect of data memorization by neural networks and improved their performance for prediction of new sequences. This effect was observed when augmentation was used simultaneously for input and the target data simultaneously. The top-5 accuracy was 84.8% for the prediction of the largest fragment (thus identifying principal transformation for classical retro-synthesis) for the USPTO-50k test dataset, and was achieved by a combination of SMILES augmentation and a beam search algorithm. The same approach provided significantly better results for the prediction of direct reactions from the single-step USPTO-MIT test set. Our model achieved 90.6% top-1 and 96.1% top-5 accuracy for its challenging mixed set and 97% top-5 accuracy for the USPTO-MIT separated set. It also significantly improved results for USPTO-full set single-step retrosynthesis for both top-1 and top-10 accuracies. The appearance frequency of the most abundantly generated SMILES was well correlated with the prediction outcome and can be used as a measure of the quality of reaction prediction.
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spelling pubmed-76431292020-11-10 State-of-the-art augmented NLP transformer models for direct and single-step retrosynthesis Tetko, Igor V. Karpov, Pavel Van Deursen, Ruud Godin, Guillaume Nat Commun Article We investigated the effect of different training scenarios on predicting the (retro)synthesis of chemical compounds using text-like representation of chemical reactions (SMILES) and Natural Language Processing (NLP) neural network Transformer architecture. We showed that data augmentation, which is a powerful method used in image processing, eliminated the effect of data memorization by neural networks and improved their performance for prediction of new sequences. This effect was observed when augmentation was used simultaneously for input and the target data simultaneously. The top-5 accuracy was 84.8% for the prediction of the largest fragment (thus identifying principal transformation for classical retro-synthesis) for the USPTO-50k test dataset, and was achieved by a combination of SMILES augmentation and a beam search algorithm. The same approach provided significantly better results for the prediction of direct reactions from the single-step USPTO-MIT test set. Our model achieved 90.6% top-1 and 96.1% top-5 accuracy for its challenging mixed set and 97% top-5 accuracy for the USPTO-MIT separated set. It also significantly improved results for USPTO-full set single-step retrosynthesis for both top-1 and top-10 accuracies. The appearance frequency of the most abundantly generated SMILES was well correlated with the prediction outcome and can be used as a measure of the quality of reaction prediction. Nature Publishing Group UK 2020-11-04 /pmc/articles/PMC7643129/ /pubmed/33149154 http://dx.doi.org/10.1038/s41467-020-19266-y Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Tetko, Igor V.
Karpov, Pavel
Van Deursen, Ruud
Godin, Guillaume
State-of-the-art augmented NLP transformer models for direct and single-step retrosynthesis
title 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_short 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
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7643129/
https://www.ncbi.nlm.nih.gov/pubmed/33149154
http://dx.doi.org/10.1038/s41467-020-19266-y
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