Applications of machine Learning to improve the efficiency and range of microbial biosynthesis: a review of state-of-art techniques

Institute for Computational Biomedicine, Department of Physiology and Biophysics, Weill Cornell Medicine of Cornell University, New York, NY, USA Key Words Machine Learning Biosynthesis Artificial Neural Networks Enzyme pathway Deep Learning DBTL cycle ART Abstract In the modern world, technology is at its peak. Different avenues in programming and technology have been explored for data analysis, automation, and robotics. Machine learning is key to optimize data analysis, make accurate predictions, and hasten/improve existing functions. Thus, presently, the field of machine learning in artificial intelligence is being developed and its uses in varying fields are being explored. One field in which its uses stand out is that of microbial biosynthesis. In this paper, a comprehensive overview of the differing machine learning programs used in biosynthesis is provided, alongside brief descriptions of the fields of machine learning and microbial biosynthesis separately. This information includes past trends, modern developments, future improvements, explanations of processes, and current problems they face. Thus, this paper's main contribution is to distill developments in, and provide a holistic explanation of, 2 key fields and their applicability to improve industry/research. It also highlights challenges and research directions, acting to instigate more research and development in the growing fields. Finally, the paper aims to act as a reference for academics performing research, industry professionals improving their processes, and students looking to understand the concept of machine learning in biosynthesis. Introduction In 1944, the field of microbial biosynthesis was first established industrially, with the antibiotic penicillin being mass produced by a fungi belonging to the Penicillium genus.[1]