ArticleJournal of cheminformatics2022
From theory to experiment: transformer-based generation enables rapid discovery of novel reactions.
Article in Journal of cheminformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed.
- Article
- ReactionT5: a pre-trained transformer model for accurate chemical reaction prediction with limited data.Journal of cheminformatics · 2025Article
- Challenging Reaction Prediction Models to Generalize to Novel Chemistry.ACS central science · 2025Article
- Application of Transformers in Cheminformatics.Journal of chemical information and modeling · 2024Review
- Generation of novel Diels-Alder reactions using a generative adversarial network.RSC advances · 2022Article
- Transformer technology in molecular science.Wiley interdisciplinary reviews. Computational molecular scienceArticle
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Authors and funding
9 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.