Clear‐Box Machine Learning for Virtual Screening of 2D Nanozymes to Target Tumor Hydrogen Peroxide

Targeting tumor hydrogen peroxide (H2O2) with catalytic materials has provided a novel chemotherapy strategy against solid tumors. Because numerous materials have been fabricated so far, there is an urgent need for an efficient in silico method, which can automatically screen out appropriate candidates from materials libraries for further therapeutic evaluation. In this work, adsorption‐energy‐based descriptors and criteria are developed for the catalase‐like activities of materials surfaces. The result enables a comprehensive prediction of H2O2‐targeted catalytic activities of materials by density functional theory (DFT) calculations. To expedite the prediction, machine learning models, which efficiently calculate the adsorption energies for 2D materials without DFT, are further developed. The finally obtained method takes advantage of both interpretability of physics model and high efficiency of machine learning. It provides an efficient approach for in silico screening of 2D materials toward tumor catalytic therapy, and it will greatly promote the development of catalytic nanomaterials for medical applications. An efficient computer‐aided method is developed to virtually select 2D materials with peroxidase and catalase‐like catalytic activities from the materials library to target tumor hydrogen peroxide. The method takes advantage of both interpretability of physics model and high efficiency of machine learning. It is expected to promote the development of catalytic nanomaterials for tumor therapy.