大模型让有机合成从预测转向全自动实验执行
Large Language Models Transform Organic Synthesis From Reaction Prediction to Automation
- 结合图神经网络与实时光谱，实现反应路径自动规划
- 可指导机器人自主实验，加速分子研发周期
- 适合化学自动化、智能制造领域的研究者参考
大型语言模型（LLMs）正重塑有机合成的实验规划与执行方式。这些基于数百万已报道反应训练的文本模型，能够提出合成路线、预测反应结果，甚至指导机器人在无人干预下执行实验。本文综述了推动LLMs从理论设想变为实验室实用伙伴的关键进展。通过将LLMs与图神经网络、量子计算和实时光谱技术结合，显著缩短了分子发现周期，支持更绿色、数据驱动的化学研究。文章也讨论了当前局限：数据偏见、推理不透明及安全机制缺失。最后，提出开放基准、联邦学习与可解释界面等社区倡议，旨在实现普惠化访问的同时，确保人类始终掌控关键决策。这些进展为人工智能驱动的快速、可靠、包容性分子创新铺平道路。
原文摘要 · Abstract (English)
Large language models (LLMs) are beginning to reshape how chemists plan and run reactions in organic synthesis. Trained on millions of reported transformations, these text-based models can propose synthetic routes, forecast reaction outcomes and even instruct robots that execute experiments without human supervision. Here we survey the milestones that turned LLMs from speculative tools into practical lab partners. We show how coupling LLMs with graph neural networks, quantum calculations and real-time spectroscopy shrinks discovery cycles and supports greener, data-driven chemistry. We discuss limitations, including biased datasets, opaque reasoning and the need for safety gates that prevent unintentional hazards. Finally, we outline community initiatives open benchmarks, federated learning and explainable interfaces that aim to democratize access while keeping humans firmly in control. These advances chart a path towards rapid, reliable and inclusive molecular innovation powered by artificial intelligence and automation.
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