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From theory to experiment: transformer-based generation enables rapid discovery of novel reactions
Deep learning methods, such as reaction prediction and retrosynthesis analysis, have demonstrated their significance in the chemical field. However, the de novo generation of novel reactions using artificial intelligence technology requires further exploration. Inspired by molecular generation, we p...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9438336/ https://www.ncbi.nlm.nih.gov/pubmed/36056425 http://dx.doi.org/10.1186/s13321-022-00638-z |
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author | Wang, Xinqiao Yao, Chuansheng Zhang, Yun Yu, Jiahui Qiao, Haoran Zhang, Chengyun Wu, Yejian Bai, Renren Duan, Hongliang |
author_facet | Wang, Xinqiao Yao, Chuansheng Zhang, Yun Yu, Jiahui Qiao, Haoran Zhang, Chengyun Wu, Yejian Bai, Renren Duan, Hongliang |
author_sort | Wang, Xinqiao |
collection | PubMed |
description | Deep learning methods, such as reaction prediction and retrosynthesis analysis, have demonstrated their significance in the chemical field. However, the de novo generation of novel reactions using artificial intelligence technology requires further exploration. Inspired by molecular generation, we proposed a novel task of reaction generation. Herein, Heck reactions were applied to train the transformer model, a state-of-art natural language process model, to generate 4717 reactions after sampling and processing. Then, 2253 novel Heck reactions were confirmed by organizing chemists to judge the generated reactions. More importantly, further organic synthesis experiments were performed to verify the accuracy and feasibility of representative reactions. The total process, from Heck reaction generation to experimental verification, required only 15 days, demonstrating that our model has well-learned reaction rules in-depth and can contribute to novel reaction discovery and chemical space exploration. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13321-022-00638-z. |
format | Online Article Text |
id | pubmed-9438336 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-94383362022-09-03 From theory to experiment: transformer-based generation enables rapid discovery of novel reactions Wang, Xinqiao Yao, Chuansheng Zhang, Yun Yu, Jiahui Qiao, Haoran Zhang, Chengyun Wu, Yejian Bai, Renren Duan, Hongliang J Cheminform Research Article Deep learning methods, such as reaction prediction and retrosynthesis analysis, have demonstrated their significance in the chemical field. However, the de novo generation of novel reactions using artificial intelligence technology requires further exploration. Inspired by molecular generation, we proposed a novel task of reaction generation. Herein, Heck reactions were applied to train the transformer model, a state-of-art natural language process model, to generate 4717 reactions after sampling and processing. Then, 2253 novel Heck reactions were confirmed by organizing chemists to judge the generated reactions. More importantly, further organic synthesis experiments were performed to verify the accuracy and feasibility of representative reactions. The total process, from Heck reaction generation to experimental verification, required only 15 days, demonstrating that our model has well-learned reaction rules in-depth and can contribute to novel reaction discovery and chemical space exploration. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13321-022-00638-z. Springer International Publishing 2022-09-02 /pmc/articles/PMC9438336/ /pubmed/36056425 http://dx.doi.org/10.1186/s13321-022-00638-z Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Article Wang, Xinqiao Yao, Chuansheng Zhang, Yun Yu, Jiahui Qiao, Haoran Zhang, Chengyun Wu, Yejian Bai, Renren Duan, Hongliang From theory to experiment: transformer-based generation enables rapid discovery of novel reactions |
title | From theory to experiment: transformer-based generation enables rapid discovery of novel reactions |
title_full | From theory to experiment: transformer-based generation enables rapid discovery of novel reactions |
title_fullStr | From theory to experiment: transformer-based generation enables rapid discovery of novel reactions |
title_full_unstemmed | From theory to experiment: transformer-based generation enables rapid discovery of novel reactions |
title_short | From theory to experiment: transformer-based generation enables rapid discovery of novel reactions |
title_sort | from theory to experiment: transformer-based generation enables rapid discovery of novel reactions |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9438336/ https://www.ncbi.nlm.nih.gov/pubmed/36056425 http://dx.doi.org/10.1186/s13321-022-00638-z |
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