Accelerating Enzyme Engineering With Artificial Intelligence in Biocatalysis: Challenges and Opportunities

Abstract

Artificial intelligence (AI) has emerged as a paradigm-shifting force in enzyme engineering, enabling data-driven prediction of catalytic activity, stability, and substrate specificity. By integrating large-scale datasets from structured databases (e.g., PDB, BRENDA) and high-throughput experimentation (e.g., deep mutational scanning, microfluidics), machine learning (ML) approaches—including random forests, support vector machines, and deep neural networks—have accelerated enzyme optimization across key domains: mutational profiling, catalytic condition refinement, and mechanistic elucidation. Notably, AlphaFold has revolutionized structure prediction, while AI-directed evolution enhanced enantioselectivity in nonnatural reactions (e.g., C─Si bond formation). Nevertheless, persistent challenges include data heterogeneity, model overfitting with sparse datasets, and limited interpretability of deep learning frameworks. Future advancements necessitate hybrid strategies merging AI with physics-based simulations (e.g., molecular dynamics), rigorous standardization of databases (aligned with FAIR principles), and synergistic integration of rational design with data-driven optimization. This review critically evaluates AI's transformative potential and methodological gaps in enzyme engineering, highlighting implications for sustainable biomanufacturing and industrial biocatalysis.

Keywords

artificial intelligence / deep learning / enzyme engineering / industrial biocatalysis / machine learning

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2025 The Author(s). Food Bioengineering published by John Wiley & Sons Australia, Ltd on behalf of State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology.