Artificial intelligence-driven metabolic engineering is applied to the development of active ingredients in Traditional Chinese Medicine

¹ School of Pharmaceutical Science and Technology, Faculty of Medicine, Tianjin University, Tianjin, 300072, China
² State Key Laboratory of Synthetic Biology, Tianjin University, Tianjin 300072, China

^* Corresponding author: drwangjuan@tju.edu.cn

Abstract

Metabolic engineering serves as a pivotal component in establishing microbial platforms for the effective biosynthesis of expensive compounds, therapeutic agents, and vegetative production systems. This field necessitates thorough comprehension of intracellular biochemical networks (encompassing molecular transformation routes and corresponding catalytic proteins). Nevertheless, the biochemical routes and critical catalysts that control numerous high-value target molecules have not been fully characterized, which is the main bottleneck for the heterologous synthesis of high-value chemicals. To address this limitation, scientists have devised optimized production circuits through the engineering of artificial biocatalysts and de novo biochemical reaction sequences. With the continuous accumulation of biological big data, the data-driven methods of artificial intelligence (AI) technology are promoting the further development of protein and metabolic pathway design. In this paper, we introduce AI-driven machine learning algorithms in prediction models, and also review recent research progress on AI-assisted metabolic engineering design and production of high-value compounds, focusing on how to use AI methods to achieve directed evolution of strains.