Data-Driven Evolutionary Design of Multienzyme-like Nanozymes

Multienzyme-like nanozymes are nanomaterials with multiple enzyme-like activities and are the focus of nanozyme research owing to their ability to facilitate cascaded reactions, leverage synergistic effects, and exhibit environmentally responsive selectivity. However, multienzyme-like nanozymes exhibit varying enzyme-like activities under different conditions, making them difficult to precisely regulate according to the design requirements. Moreover, individual enzyme-like activity in a multienzyme-like activity may accelerate, compete, or antagonize each other, rendering the overall activity a complex interplay of these factors rather than a simple sum of single enzyme-like activity. A theoretically guided strategy is highly desired to accelerate the design of multienzyme-like nanozymes. Herein, nanozyme information was collected from 4159 publications to build a nanozyme database covering element type, element ratio, chemical valence, shape, pH, etc. Based on the clustering correlation coefficients of the nanozyme information, the material features in distinct nanozyme classifications were reorganized to generate compositional factors for multienzyme-like nanozymes. Moreover, advanced methods were developed, including the quantum mechanics/molecular mechanics method for analyzing the surface adsorption and binding energies of substrates, transition states, and products in the reaction pathways, along with machine learning algorithms to identify the optimal reaction pathway, to aid the evolutionary design of multienzyme-like nanozymes. This approach culminated in creating CuMnCo