Chromatographic separation technology has been widely applied in pharmaceutical, chemical, and food industries due to its high efficiency. However, traditional human-dependent chromatographic process development faces challenges such as reliance on expert experience, long development cycles, and labor intensity. ChromR, a large language model (LLM)-driven platform for chromatographic process design and optimization, is presented in this work. The platform integrates ChromLLM, a domain-specific LLM trained for chromatography, along with a multi-agent system and an automated chromatographic experimental device. The multi-agent system comprises four agents: domain knowledge answering, experimental design, experimental execution, and data analysis. ChromR enables automatic completion of the entire workflow-including initial process parameter recommendation, experimental design, automated execution, data analysis, and multi-objective optimization. By utilizing ChromR, dependency on expert knowledge is effectively reduced, while labor input and development time are significantly decreased. Chromatographic purification of the extract of Ginkgo biloba leaf (EGBL) was selected as a case study. ChromR successfully developed a chromatographic process within one week that meets multiple objectives, including fraction quality and production efficiency, reducing development time to approximately one-seventh of that required by the conventional paradigm. An intelligent, automated, and universally applicable new paradigm was established for chromatographic process development.

翻译：色谱分离技术因其高效性已在制药、化工和食品工业中得到广泛应用。然而，传统依赖人工的色谱工艺开发面临诸多挑战，如对专家经验的依赖、开发周期长以及劳动强度大。本文提出了ChromR，一个由大语言模型驱动的色谱工艺设计与优化平台。该平台集成了为色谱领域专门训练的大语言模型ChromLLM、一个多智能体系统以及一套自动化色谱实验装置。多智能体系统包含四个智能体：领域知识问答、实验设计、实验执行与数据分析。ChromR能够自动完成包括初始工艺参数推荐、实验设计、自动化执行、数据分析以及多目标优化在内的完整工作流程。通过使用ChromR，可有效降低对专家知识的依赖，同时显著减少人力投入与开发时间。研究以银杏叶提取物的色谱纯化作为案例。ChromR在一周内成功开发出满足馏分质量与生产效率等多重目标的色谱工艺，将开发时间缩短至传统范式所需时间的约七分之一。这为色谱工艺开发建立了一种智能、自动化且普遍适用的新范式。