Abstract This study systematically explores the applications of artificial intelligence (AI) in enzyme research, emphasizing its role in enzyme design, catalytic function prediction, stability, discovery of novel enzymes, and process optimization. Using bibliometric review techniques, we quantified research subfields, mapped global scientific trends, and identified the machine learning (ML) approaches most frequently employed in enzymology. Our analysis reveals that algorithms such as random forests, support vector machines, decision trees, and K‐nearest neighbours are predominantly applied to predict protein solubility, enzyme stability after mutations, catalytic mechanisms, and drug‐enzyme interactions. These findings demonstrate how AI enables more accurate predictions of enzymatic behaviour, accelerates drug discovery pipelines, and supports the identification of new metabolic pathways for industrial biotechnology. Beyond mapping current applications, this work highlights significant challenges, including the scarcity of high‐quality datasets and the limited generalization of models across enzyme classes. Nonetheless, the results underscore the transformative potential of AI when integrated into experimental workflows, particularly with advances in laboratory automation and the emerging synergy between AI and quantum computing. By consolidating state‐of‐the‐art knowledge and identifying future directions, this study contributes to guiding research strategies in data‐driven enzyme engineering and promotes the development of sustainable biotechnological solutions.
Sales et al. (Mon,) studied this question.