Prediction of Enzyme Functions by Artificial Intelligence

Rapid and accurate classification of enzyme functions is crucial in biological and biotechnological research, as it enables the design of novel biosynthetic pathways, the optimization of cellular metabolism, and the discovery of new biocatalysts for industrial applications. The advent of computational methods has facilitated high-throughput analysis of enzyme functions, substantially reducing time, labor, and cost. Moreover, artificial intelligence has enabled the automatic learning of intrinsic enzyme features, significantly enhancing the performance and scalability of computational predictions. In this chapter, we review the evolution of computational strategies for enzyme function prediction, tracing the progress from early bioinformatics-based approaches to deep learning-based methodologies. In particular, we highlight how recent advances in deep learning algorithms have enabled more efficient and accurate characterization of enzyme properties directly from raw data. We anticipate that enzyme function classification powered by artificial intelligence will significantly accelerate research across multiple areas of biotechnology, including metabolic engineering and synthetic biology.