Xuan Wenjie, Li Xiaofo, Gao Honglei, Zhang Luyao, Hu Jili, Sun Liping, Kan Hongxing
School of Medical Informatics Engineering, Anhui University of Chinese Medicine, Hefei, China.
Center for Xin'an Medicine and Modernization of Traditional Chinese Medicine of IHM, Anhui University of Chinese Medicine, Hefei, China.
Sci Rep. 2025 Apr 17;15(1):13305. doi: 10.1038/s41598-025-96815-9.
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, V), 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, V, K) with an R 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.
传统的纳米酶合成方法通常既耗时又复杂，影响了效率。人工智能（AI）有潜力简化这些过程，但专门的纳米酶数据库非常少，限制了研究和应用的资源。为了弥补这一差距，我们开发了AI-ZYMES，这是一个全面的纳米酶数据库，包含1085条记录和400种纳米酶。该平台纳入了多项关键创新，使其有别于现有数据库：首先，标准化的数据整理：AI-ZYMES解决了催化指标（如K、V）、形态和分散体系方面的不一致问题，能够进行可靠的跨研究比较，这是DiZyme和nanozymes.net等现有资源所缺乏的。其次，双重人工智能框架：梯度提升回归器预测动力学常数（K、V、K），R值高达0.85，而AdaBoost分类器仅根据纳米酶名称识别酶模拟活性，在预测准确性方面超过了传统的随机森林模型。最后，基于ChatGPT的合成助手：该平台包括一个人工智能驱动的文献提取助手（准确率67.55%）和通过语义分析生成合成途径的助手（准确率90%）。这减少了人工工作量，并将大语言模型输出中的错误降至最低，确保了高质量的结果。这些创新使AI-ZYMES成为加速纳米酶研究和应用的宝贵工具，包括抗菌治疗、生物传感和环境修复。该平台提高了数据可及性，减少了实验冗余，并加快了从发现到实际应用的转化。通过弥合现有系统的数据碎片化和预测局限性，AI-ZYMES为人工智能驱动的纳米材料进步树立了新的标杆。
