Abstract Natural enzymes serve as highly efficient biocatalysts yet exhibit limited stability under physiological conditions, including sensitivity to body temperature, narrow pH tolerance, and vulnerability to protease degradation, alongside high preparation and purification costs. Artificial biocatalysts, as highly efficient biocalysts, offer a promising alternative. However, their catalytic activity and selectivity remain inferior to those of natural enzymes, hindering clinical translation. To overcome these limitations, intelligent design strategies have been developed to achieve precise control over the structure and function of artificial biocatalysts. This review summarizes recent progress in the intelligent design of artificial enzymes for biomedical applications. Three major strategies are discussed including biomimetic structural engineering based on atomic and ligand modulation, environment‐responsive design that utilizes endogenous pathological cues or exogenous physical stimuli, and artificial intelligence‐assisted rational design incorporating machine learning and high‐throughput computation. The application of these strategies in treating chronic diseases, including metabolic disorders, neurological diseases, tumors, and infections, is also reviewed. Finally, current challenges and future directions are discussed to inform the rational development and clinical translation of high‐performance artificial enzymes.
Zhang et al. (Mon,) studied this question.