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Large language model agents as experimental orchestrators in data-driven enzyme engineering
Yuan Gao1, Lihao Fu2, Tong Si1
1State Key Laboratory for Quantitative Synthetic Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China; University of Chinese Academy of Sciences, Beijing 100049, China.
Abstract:
Artificial intelligence (AI) has revolutionized computational enzyme design, but experimental validation remains a critical bottleneck. This perspective examines how large language model (LLM) agents bridge the gap between computational predictions and physical experimentation through intelligent orchestration of complex experimental workflows. Unlike traditional automation following rigid scripts, LLM-powered systems dynamically generate protocols, translate them into machine commands, coordinate resources, and adapt to experimental feedback in real-time. This short perspective covers multi-agent frameworks from recent implementations and presents case studies showcasing closed-loop design-build-test-learn iterations in enzyme engineering. These systems provide both standardization and flexibility needed to handle complex, context-dependent experimental decisions, potentially accelerating enzyme engineering while enhancing accessibility to sophisticated AI capabilities.
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