Machine Learning: A Suitable Method for Biocatalysis

Abstract

Biocatalysis is currently a workhorse used to produce a wide array of compounds, from bulk to fine chemicals, in a green and sustainable manner. The success of biocatalysis is largely thanks to an enlargement of the feasible chemical reaction toolbox. This materialized due to major advances in enzyme screening tools and methods, together with high-throughput laboratory techniques for biocatalyst optimization through enzyme engineering. Therefore, enzyme-related knowledge has significantly increased. To handle the large number of data now available, computational approaches have been gaining relevance in biocatalysis, among them machine learning methods (MLMs). MLMs use data and algorithms to learn and improve from experience automatically. This review intends to briefly highlight the contribution of biocatalysis within biochemical engineering and bioprocesses and to present the key aspects of MLMs currently used within the scope of biocatalysis and related fields, mostly with readers non-skilled in MLMs in mind. Accordingly, a brief overview and the basic concepts underlying MLMs are presented. This is complemented with the basic steps to build a machine learning model and followed by insights into the types of algorithms used to intelligently analyse data, identify patterns and develop realistic applications in biochemical engineering and bioprocesses. Notwithstanding, and given the scope of this review, some recent illustrative examples of MLMs in protein engineering, enzyme production, biocatalyst formulation and enzyme screening are provided, and future developments are suggested. Overall, it is envisaged that the present review will provide insights into MLMs and how these are major assets for more efficient biocatalysis.

Advances, Synergy, and Perspectives of Machine Learning and Biobased Polymers for Energy, Fuels, and Biochemicals for a Sustainable Future

“…Future studies could develop ML models that simulate the interactions between biopolymers and various pharmaceutical compounds, aiding in creating more effective and safer drug delivery systems. Furthermore, the exploration of ML applications in biocatalysis within biochemical production is deemed promising, likely leading to enhanced productivity and sustainability in biochemical production. ,, …”

“…Furthermore, the exploration of ML applications in biocatalysis within biochemical production is deemed promising, likely leading to enhanced productivity and sustainability in biochemical production. 189,191,206 Finally, the future of ML and biopolymers research lies in collaborative and cross-disciplinary efforts. Combining the expertise of chemists, material scientists, computer scientists, and engineers will be key to advancing this field.…”

“…ML further finds its application in biocatalysis, a significant aspect of biochemical production. − Through ML algorithms, the most suitable catalysts for specific biochemical reactions are efficiently identified, ensuring that the reactions proceed optimally. This efficiency leads to enhanced productivity and sustainability in biochemical production.…”

Latest Trends in Lipase-Catalyzed Synthesis of Ester Carbohydrate Surfactants: From Key Parameters to Opportunities and Future Development

“…The exponential increase in knowledge about enzymes, combined with technological and biological advances, means that complex genomic and proteomic databases can be created. This data can then be processed by artificial intelligence (AI), with predictive functional analysis, imposing discriminating criteria, to perform screening [177,178]. Simply considering machine learning, which is a subset of AI, 2 approaches are possible for predicting a synthesis [179]: non-supervised learning and supervised one.…”