通過反應描述語言橋接化學和人工智能

隨著人工智能的快節奏發展，大型語言模型越來越多地用於應對各種科學挑戰。

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.

隨著人工智能（AI）的快速發展，大型語言模型（LLM）越來越多地用於應對各種科學挑戰。此過程中的關鍵步驟是將特定於域的數據轉換為適合LLM的格式，通常是一個令牌序列。在化學中，分子通常用分子線性符號表示，化學反應被描述為反應物和產物對。但是，這種方法並未捕獲反應過程中發生的原子和鍵變化，這對於化學理解和操縱至關重要。為了彌合這一差距並促進化學與LLM之間的無縫整合，我們引入了ReactSeq，一種反應說明語言，將化學反應分解為一系列分子編輯操作。

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.

每個Reactseq令牌都對應於特定的原子或鍵修的，使化學轉化的分步展開。我們使用ReactSeq培訓了一種用於逆合合成預測的語言模型，並觀察到它在所有基準測試中始終優於現有方法。此外，該模型表現出了有希望的緊急能力，例如對用戶請求進行多步合成計劃，並為其預測提供解釋。為了深入研究LLM在導航化學空間中的能力，我們訓練了一個模型，以根據ReactSeq表示，預測反應產量，並在此任務中實現了高性能。

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.

我們的分析表明，該模型學會了基於化學原理評估反應的可行性，這突出了LLM超出經驗模式並對數據產生化學理解的潛力。最後，我們使用ReactSeq生成了化學反應的通用和可靠表示，從而有助於從文獻數據庫中有效檢索相關的實驗程序。這種能力為理論預測和實驗觀察結果之間的無縫整合鋪平了道路，最終推進了化學發現和發明。