Large language models (LLMs) have emerged as powerful tools in chemistry, significantly impacting molecule design, property prediction, and synthesis optimization. This review highlights LLM capabilities in these domains and their potential to accelerate scientific discovery through automation. We also review LLM-based autonomous agents: LLMs with a broader set of tools to interact with their surrounding environment. These agents perform diverse tasks such as paper scraping, interfacing with automated laboratories, and synthesis planning. As agents are an emerging topic, we extend the scope of our review of agents beyond chemistry and discuss across any scientific domains. This review covers the recent history, current capabilities, and design of LLMs and autonomous agents, addressing specific challenges, opportunities, and future directions in chemistry. Key challenges include data quality and integration, model interpretability, and the need for standard benchmarks, while future directions point towards more sophisticated multi-modal agents and enhanced collaboration between agents and experimental methods. Due to the quick pace of this field, a repository has been built to keep track of the latest studies: https://github.com/ur-whitelab/LLMs-in-science.

中文翻译：

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化学中的大型语言模型和自主代理综述


大型语言模型 （LLMs） 已成为化学领域的强大工具，对分子设计、性质预测和合成优化产生了重大影响。这篇综述重点介绍了 LLM 在这些领域的能力及其通过自动化加速科学发现的潜力。我们还回顾了基于 LLM 的自主代理：LLMs，以及更广泛的工具集来与周围环境交互。这些试剂执行各种任务，例如纸张抓取、与自动化实验室接口和合成计划。由于药物是一个新兴的话题，我们将药物的审查范围扩展到化学之外，并在任何科学领域进行讨论。本综述涵盖了 LLMs，解决了化学领域的具体挑战、机遇和未来方向。主要挑战包括数据质量和集成、模型可解释性以及对标准基准的需求，而未来的方向指向更复杂的多模态代理以及增强代理与实验方法之间的协作。由于该领域的发展速度很快，已经建立了一个存储库来跟踪最新的研究：https://github.com/ur-whitelab/LLMs。