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.

中文翻译：

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从理论到实验：基于变压器的生成能够快速发现新反应


反应预测和逆合成分析等深度学习方法已经证明了它们在化学领域的重要性。然而，利用人工智能技术从头生成新反应还需要进一步探索。受分子生成的启发，我们提出了反应生成的新任务。在此，应用 Heck 反应来训练 Transformer 模型（一种最先进的自然语言处理模型），在采样和处理后生成 4717 个反应。然后，通过组织化学家判断所产生的反应，确认了2253个新颖的赫克反应。更重要的是，进行了进一步的有机合成实验来验证代表性反应的准确性和可行性。从Heck反应生成到实验验证的整个过程仅需要15天，这表明我们的模型具有深入学习的反应规则，可以为新的反应发现和化学空间探索做出贡献。