Machine Learning for Microbial Cell Factories: Pathway Design, Enzyme Engineering, and Metabolic Regulation

Microbial cell factories represent sustainable platforms for the production of fuels, chemicals, and therapeutics, but their development is limited by challenges in pathway discovery, enzyme optimization, and metabolic regulation. Recent advances in artificial intelligence and machine learning are reshaping this field by enabling predictive pathway design, enhanced protein engineering, and dynamic network regulation. Emerging strategies such as graph neural networks, generative models, and reinforcement learning (RL) now allow systematic exploration of vast design spaces with enhanced accuracy and scalability. This review highlights recent advancements in microbial engineering. It discusses how AI-driven frameworks are advancing the field from experience-guided and rule-based engineering toward data-driven, model-assisted, and increasingly autonomous workflows. These changes lay the foundation for next-generation biomanufacturing.