摘要：Revolutionary breakthroughs in artificial intelligence (AI) and machine learning (ML) have had a profound impact on a widerange of scientific disciplines, including the development of artificial cell factories for biomanufacturing. In this paper, wereview the latest studies on the application of data-driven methods for the design of new proteins, pathways, and strains. Wefirst briefly introduce the various types of data and databases relevant to industrial biomanufacturing, which are the basis fordata-driven research. Different types of algorithms, including traditional ML and more recent deep learning methods, are alsopresented. We then demonstrate how these data-based approaches can be applied to address various issues in cell factorydevelopment using examples from recent studies, including the prediction of protein function, improvement of metabolicmodels, and estimation of missing kinetic parameters, design of non-natural biosynthesis pathways, and pathway *** the last section, we discuss the current limitations of these data-driven approaches and propose that data-driven methodsshould be integrated with mechanistic models to complement each other and facilitate the development of synthetic strains forindustrial biomanufacturing.