Machine Learning-Guided Enzyme Engineering Approaches for Enhanced Biocatalytic Efficiency: Concepts, Mechanisms, and Future Directions

Biocatalysis has emerged as a mainstay in the field of sustainable chemical synthesis owing to its high selectivity, mild reaction conditions, and reduced environmental impact. Traditional enzyme engineering approaches, such as rational design and directed evolution, are often associated with limited throughput and a limited understanding of sequence–structure–function relationships, despite high experimental costs. In recent years, the integration of machine learning (ML) into enzyme engineering has emerged as a transformative approach, enabling data-driven prediction, design, and optimization of biocatalysts, thereby enhancing performance and applications. This review provides a comprehensive overview of ML-guided strategies to improve key enzymatic parameters, including the turnover number (kcat), substrate affinity (Km), and catalytic efficiency (kcat/Km), with a focus on mechanistic insights and performance outcomes. The integration of ML models into design–build–test–learn (DBTL) cycles accelerated directed evolution, reduced screening efforts, and enabled targeted mutagenesis. Beyond applications, this review also discusses the current limitations of ML-guided approaches, including data scarcity, model interpretability, and challenges in predicting complex mutations and allosteric effects. The gap between computational predictions and experimental outcomes is identified, and the role of ML integration with enzyme kinetics, molecular dynamics, and high-throughput experimentation is emphasized. Future directions, such as generative AI, explainable ML, and autonomous laboratories, are discussed for next-generation biocatalytic applications.