通过反应描述语言桥接化学和人工智能

随着人工智能的快节奏发展，大型语言模型越来越多地用于应对各种科学挑战。

With the rapid development of artificial intelligence (AI), large language models (LLMs) are increasingly being used to address various scientific challenges. A crucial step in this process is converting domain-specific data into a format suitable for LLMs, typically a sequence of tokens. In chemistry, molecules are commonly represented by molecular linear notations, and chemical reactions are depicted as pairs of reactants and products. However, this approach does not capture the atomic and bond changes that occur during reactions, which are essential for chemical understanding and manipulation. To bridge this gap and facilitate seamless integration between chemistry and LLMs, we introduce ReactSeq, a reaction description language that decomposes chemical reactions into a series of molecular editing operations.

随着人工智能（AI）的快速发展，大型语言模型（LLM）越来越多地用于应对各种科学挑战。此过程中的关键步骤是将特定于域的数据转换为适合LLM的格式，通常是一个令牌序列。在化学中，分子通常用分子线性符号表示，化学反应被描述为反应物和产物对。但是，这种方法并未捕获反应过程中发生的原子和键变化，这对于化学理解和操纵至关重要。为了弥合这一差距并促进化学与LLM之间的无缝整合，我们引入了ReactSeq，一种反应说明语言，将化学反应分解为一系列分子编辑操作。

Each ReactSeq token corresponds to a specific atomic or bond modification, enabling a step-by-step unfolding of the chemical transformation. We trained a language model for retrosynthesis prediction using ReactSeq and observed that it consistently outperformed existing methods in all benchmark tests. Furthermore, the model demonstrated promising emergent abilities, such as performing multistep synthesis planning in response to user requests and providing explanations for its predictions. To delve deeper into the capabilities of LLMs in navigating chemical space, we trained a model to predict reaction yield based on ReactSeq representations and achieved high performance in this task.

每个Reactseq令牌都对应于特定的原子或键修的，使化学转化的分步展开。我们使用ReactSeq培训了一种用于逆合合成预测的语言模型，并观察到它在所有基准测试中始终优于现有方法。此外，该模型表现出了有希望的紧急能力，例如对用户请求进行多步合成计划，并为其预测提供解释。为了深入研究LLM在导航化学空间中的能力，我们训练了一个模型，以根据ReactSeq表示，预测反应产量，并在此任务中实现了高性能。

Our analysis indicates that the model learned to evaluate the feasibility of reactions based on chemical principles, highlighting the potential of LLMs to go beyond empirical patterns and develop a chemical understanding of the data. Finally, we used ReactSeq to generate universal and reliable representations of chemical reactions, facilitating efficient retrieval of relevant experimental procedures from literature databases. This capability paves the way for seamless integration between theoretical predictions and experimental observations, ultimately advancing chemical discovery and invention.

我们的分析表明，该模型学会了基于化学原理评估反应的可行性，这突出了LLM超出经验模式并对数据产生化学理解的潜力。最后，我们使用ReactSeq生成了化学反应的通用和可靠表示，从而有助于从文献数据库中有效检索相关的实验程序。这种能力为理论预测和实验观察结果之间的无缝整合铺平了道路，最终推进了化学发现和发明。