从文字到分子：化学领域大语言模型综述
From Words to Molecules: A Survey of Large Language Models in Chemistry
AI总结：
本文综述了将大语言模型应用于化学领域的方法，涵盖分子表示、模型分类、预训练目标及应用，并展望了未来研究方向。
摘要：
近年来，大语言模型（LLMs）在自然语言处理（NLP）及多个跨学科领域取得了显著成功。然而，将LLMs应用于化学是一项需要专门领域知识的复杂任务。本文深入探讨了将LLMs整合到化学领域中所采用的细致方法论，剖析了这一跨学科交汇处的复杂性与创新点。具体而言，我们的分析首先考察了通过不同的表示和分词方法将分子信息输入LLMs的方式。随后，我们根据输入数据的领域和模态将化学LLMs分为三个不同的类别，并讨论了整合这些输入以供LLMs使用的方法。此外，本文深入探讨了针对化学LLMs调整的预训练目标。之后，我们探索了LLMs在化学中的多样化应用，包括其在化学任务中的新型应用范式。最后，我们指出了有前景的研究方向，包括与化学知识的进一步整合、持续学习的进展以及模型可解释性的改进，为该领域的突破性发展铺平道路。
英文摘要：
In recent years, Large Language Models (LLMs) have achieved significant success in natural language processing (NLP) and various interdisciplinary areas. However, applying LLMs to chemistry is a complex task that requires specialized domain knowledge. This paper provides a thorough exploration of the nuanced methodologies employed in integrating LLMs into the field of chemistry, delving into the complexities and innovations at this interdisciplinary juncture. Specifically, our analysis begins with examining how molecular information is fed into LLMs through various representation and tokenization methods. We then categorize chemical LLMs into three distinct groups based on the domain and modality of their input data, and discuss approaches for integrating these inputs for LLMs. Furthermore, this paper delves into the pretraining objectives with adaptations to chemical LLMs. After that, we explore the diverse applications of LLMs in chemistry, including novel paradigms for their application in chemistry tasks. Finally, we identify promising research directions, including further integration with chemical knowledge, advancements in continual learning, and improvements in model interpretability, paving the way for groundbreaking developments in the field.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
