From theory to experiment: transformer-based generation enables rapid discovery of novel reactions

Xinqiao Wang, Chuansheng Yao, Yun Zhang, Jiahui Yu, Haoran Qiao, Chengyun Zhang, Yejian Wu, Renren Bai, Hongliang Duan

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Article number60
JournalJournal of Cheminformatics
Volume14
Issue number1
DOIs
Publication statusPublished - Dec 2022
Externally publishedYes

Keywords

  • Deep learning
  • Heck reactions
  • Reaction generation

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