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<p>Traditional methods for synthesizing nanozymes are often time-consuming and complex, hindering efficiency. Artificial intelligence (AI) has the potential to simplify these processes, but there are very few dedicated nanozyme databases available, limiting the resources for research and application. To address this gap, we developed AI-ZYMES, a comprehensive nanozyme database featuring 1,085 entries and 400 types of nanozymes. The platform incorporates several key innovations that distinguish it from existing databases: Firstly, standardized Data Curation: AI-ZYMES resolves inconsistencies in catalytic metrics (e.g., K<sub>m</sub>, V<sub>max</sub>), morphologies, and dispersion systems, enabling reliable cross-study comparisons, something that existing resources like DiZyme and nanozymes.net lack.Secondly, dual AI Framework: A gradient-boosting regressor predicts kinetic constants (K<sub>m</sub>, V<sub>max</sub>, K<sub>cat</sub>) with an R<sup>2</sup> up to 0.85, while an AdaBoost classifier identifies enzyme-mimicking activities based solely on nanozyme names, surpassing traditional random forest models in predictive accuracy.Lastly, ChatGPT-based Synthesis Assistant: The platform includes an AI-driven assistant for literature extraction (67.55% accuracy) and synthesis pathway generation via semantic analysis (90% accuracy). This reduces manual effort and minimizes errors in large language model outputs, ensuring high-quality results.These innovations make AI-ZYMES a valuable tool for accelerating nanozyme research and application, including antimicrobial therapy, biosensing, and environmental remediation. The platform improves data accessibility, reduces experimental redundancy, and speeds up the translation of discoveries into practical use. By bridging the data fragmentation and predictive limitations of existing systems, AI-ZYMES establishes a new benchmark for AI-driven advancements in nanomaterials.</p>

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Artificial intelligence driven platform for rapid catalytic performance assessment of nanozymes

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月17日

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  • Wenjie Xuan,
  • Xiaofo Li,
  • Honglei Gao,
  • Luyao Zhang,
  • Jili Hu,
  • Liping Sun,
  • Hongxing Kan

摘要

Traditional methods for synthesizing nanozymes are often time-consuming and complex, hindering efficiency. Artificial intelligence (AI) has the potential to simplify these processes, but there are very few dedicated nanozyme databases available, limiting the resources for research and application. To address this gap, we developed AI-ZYMES, a comprehensive nanozyme database featuring 1,085 entries and 400 types of nanozymes. The platform incorporates several key innovations that distinguish it from existing databases: Firstly, standardized Data Curation: AI-ZYMES resolves inconsistencies in catalytic metrics (e.g., Km, Vmax), morphologies, and dispersion systems, enabling reliable cross-study comparisons, something that existing resources like DiZyme and nanozymes.net lack.Secondly, dual AI Framework: A gradient-boosting regressor predicts kinetic constants (Km, Vmax, Kcat) with an R2 up to 0.85, while an AdaBoost classifier identifies enzyme-mimicking activities based solely on nanozyme names, surpassing traditional random forest models in predictive accuracy.Lastly, ChatGPT-based Synthesis Assistant: The platform includes an AI-driven assistant for literature extraction (67.55% accuracy) and synthesis pathway generation via semantic analysis (90% accuracy). This reduces manual effort and minimizes errors in large language model outputs, ensuring high-quality results.These innovations make AI-ZYMES a valuable tool for accelerating nanozyme research and application, including antimicrobial therapy, biosensing, and environmental remediation. The platform improves data accessibility, reduces experimental redundancy, and speeds up the translation of discoveries into practical use. By bridging the data fragmentation and predictive limitations of existing systems, AI-ZYMES establishes a new benchmark for AI-driven advancements in nanomaterials.