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

TL;DR

This review discusses how machine learning techniques are advancing microbial biosynthesis by enhancing efficiency and expanding capabilities, highlighting current trends, challenges, and future research directions.

Contribution

It provides a comprehensive overview of machine learning applications in microbial biosynthesis, integrating developments, challenges, and potential future improvements.

Findings

Machine learning improves microbial biosynthesis efficiency.

Current challenges include data quality and model interpretability.

Future research directions involve integrating advanced algorithms.

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.