Artificial intelligence‐assisted nanozyme design for medical and environmental applications
TL;DR
Computational and data‐driven paradigms for rational nanozyme engineering, encompassing cross‐source data infrastructures, physicochemical feature representation, model architectures, and closed‐loop experimental validation are summarized.
Abstract
Nanozymes, a class of nanomaterials with intrinsic enzyme‐like catalytic activities, have emerged as promising platforms for catalysis‐driven diagnostics, therapeutics, and environmental sensing. Yet the multiscale structural heterogeneity of nanozymes and the nonlinear coupling between physicochemical descriptors and catalytic performance have hindered the establishment of predictive structure‐activity relationships. The rapid integration of artificial intelligence, particularly machine learning and deep learning, is reshaping this landscape by enabling systematic data integration, descriptor engineering, quantitative performance prediction, and mechanism‐informed optimization. This Review summarizes computational and data‐driven paradigms for rational nanozyme engineering, encompassing cross‐source data infrastructures, physicochemical feature representation, model architectures, and closed‐loop experimental validation. Representative advances in AI‐guided nanozyme design and high‐throughput computational screening are highlighted, alongside emerging applications in intelligent diagnostics, precision therapeutics, and environmental surveillance. By bridging materials science, catalysis theory, and data intelligence, AI‐enabled strategies provide a coherent framework for accelerating nanozyme discovery and functional optimization, paving the way toward more precise and scalable catalytic nanomaterials.