From Theory to Experiment: Transformer-Based Generation Enables Rapid Discovery of Novel Reactions

10 August 2021, Version 2
This content is a preprint and has not undergone peer review at the time of posting.


Deep learning methods have been proven their potential roles in the chemical field, such as reaction prediction and retrosynthesis analysis. However, the de novo generation of unreported reactions using artificial intelligence technology remains not be completely explored. Inspired by molecular generation, we proposed the task of novel reaction generation. In this work, we applied the Heck reactions to train the transformer model, state-of-art natural language process model and obtained 4717 generated reactions after sampling and processing. We then confirmed that 2253 novel Heck reactions by organizing chemists to judge the generated reactions, and adopted organic synthesis experiment to verify the feasibility of unreported reactions. In this process, it only took 15 days from Heck reaction generation to experimental verification, proving that our model learns reaction rules in-depth and can make great contributions in the novel reaction discovery.


Deep learning
Heck reactions
Reaction generation
Organic chemistry

Supplementary materials

From theory to experiment: transformer-based generation enables rapid discovery of novel reactions
Supplementary materilas for from theory to experiment: transformer-based generation enables rapid discovery of novel reactions


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