Machine learning in predictive biocatalysis: A comparative review of methods and applications
Neha Tripathi, Joan Hérisson, Jean‐Loup Faulon
Abstract
In recent years, machine learning has significantly advanced predictive biocatalysis, enabling innovative approaches to enzyme function prediction, biocatalyst discovery, reaction modeling, and metabolic pathway optimization. This review provides a comparative analysis of current methodologies, highlighting the intersection between computational tools and biochemical data for predictive biocatalysis applications. Key aspects covered include enzyme classification, reaction annotation, enzyme-substrate specificity, reaction outcomes, and kinetic parameter prediction. We discuss various machine learning approaches, such as neural networks with increased depth, convolutional networks, graph-based architectures, and transformer models, highlighting their respective strengths and limitations. The integration of large-scale data, representation and featurization techniques, and robust validation methods has accelerated enzyme discovery and the development of eco-friendly, sustainable biocatalytic processes. In the future, machine learning is anticipated to play a central role in connecting computational insights with practical enzyme engineering efforts, advancing applications in synthetic biology, metabolic engineering, and green biocatalysis.