DiZyme: The Ultimate Resource for Nanozyme Multiple Catalytic Activity Prediction

Abstract

Abstract Nanozymes are unique materials with many valuable properties for applications in biomedicine, biosensing, clinical diagnostics, environmental monitoring and beyond. However, it is usually challenging to find new nanozymes. In this work, we developed a machine learning (ML) approach to search for new nanozymes and deployed a web platform DiZyme, featuring a unique database of nanozymes, catalytic activity prediction, data visualization and DiZyme Assistant interface powered by a large language model (LLM). For the first time, we enable prediction of multiple catalytic activities of nanozymes by training an ensemble learning algorithm achieving R2 = 0.75 for the Michaelis-Menten constant and R2 = 0.77 for the maximum velocity. We envision accurate prediction of multi-catalytic activity (peroxidase, oxidase, and catalase) promoting entirely novel applications for a wide range of surface-modified inorganic nanozyme compositions. The DiZyme state-of-the-art database of nanozymes contains 1210 experimental samples with a wide range of compositions and molecular features. The DiZyme Assistant based on the ChatGPT model provides users with supporting information on experimental samples, such as synthesis procedures, measurement protocols, etc. DiZyme (dizyme.aicidlab.itmo.ru) is now openly available worldwide.