用大模型自动解析自然语言生成化学反应动力学，加速系统生物学模拟。
Integrating Large Language Models For Monte Carlo Simulation of Chemical Reaction Networks
- 大模型解析自然语言描述，自动生成反应动力学参数
- 在Copasi工具中集成，实现从描述到仿真的无缝衔接
- 适合生物建模者快速构建复杂反应网络
化学反应网络是建模和探索复杂生物过程、生化相互作用及系统生物学中不同动态行为的重要方法。但构建此类反应动力学需耗费大量时间。本文利用现代大语言模型的高效性，自动化执行化学反应网络的随机蒙特卡洛仿真，并通过自然语言描述直接启动仿真。同时，该流程已集成至广泛使用的仿真工具 COPASI，为建模者与研究人员提供便利。本研究展示了现代大语言模型在解析和生成复杂化学反应过程动力学方面的有效性与局限性。
原文摘要 · Abstract (English)
Chemical reaction network is an important method for modeling and exploring complex biological processes, bio-chemical interactions and the behavior of different dynamics in system biology. But, formulating such reaction kinetics takes considerable time. In this paper, we leverage the efficiency of modern large language models to automate the stochastic monte carlo simulation of chemical reaction networks and enable the simulation through the reaction description provided in the form of natural languages. We also integrate this process into widely used simulation tool Copasi to further give the edge and ease to the modelers and researchers. In this work, we show the efficacy and limitations of the modern large language models to parse and create reaction kinetics for modelling complex chemical reaction processes.
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