重庆医科大学陈陶团队的最新研究提出了光热金属-酚网络的机器学习辅助预测。相关论文发表在2025年1月9日出版的《德国应用化学》杂志上。

光热疗法（PTT）在癌症治疗、伤口愈合和抗菌治疗方面显示出巨大的潜力，其疗效在很大程度上取决于光热剂（PTA）的性能。金属-酚网络（MPN）材料由于其低廉的成本、良好的生物相容性，和优异的配体-金属电荷转移性能，而成为理想的PTA候选材料。

然而，并非所有MPN都表现出显著的光热性能，而且MPN的巨大化学空间（超过70万种潜在组合）使高光热材料的筛选变得复杂。为了解决这一挑战，本研究引入了机器学习（ML）方法来预测MPN的光热性能。

研究者构建了80个模块化MPN的光热特性数据库，并通过特征工程和模型训练对ML过程进行了优化。选择的极端梯度增强模型（XGBoost）成功地从44438个虚拟数据库中，识别出1654个高光热mpn。随后的实验验证显示，预测高光热MPN的成功率高达70%。此外，还发现了一些以前未报道的高光热MPN，显示了光热抗菌应用的优势。

本研究为高效筛选MPN材料提供了一种创新的机器学习驱动方法，为PTT和其他生物医学应用中的PTA设计提供了坚实的基础。

附：英文原文

Title: Machine Learning-Assisted Prediction of Photothermal Metal-Phenolic Networks

Author: Dongqi Fan, Xu Chen, Shan Wang, Jinglei Zhan, Yuan Chen, Houqi Zhou, Dize Li, Han Tang, Qingqing He, Tao Chen

Issue&Volume: 2025-01-09

Abstract: Photothermal therapy (PTT) demonstrates significant potential in cancer treatment, wound healing, and antibacterial therapy, with its efficacy largely depending on the performance of photothermal agents (PTAs). Metal-phenolic network (MPN) materials are ideal PTA candidates due to their low cost, good biocompatibility and excellent ligand-to-metal charge transfer properties. However, not all MPNs exhibit significant photothermal properties, and the vast chemical space of MPNs (over 700,000 potential combinations) complicates the screening of high-photothermal materials. To address this challenge, this study introduces machine learning (ML) methods for predicting the photothermal performance of MPNs. A database of photothermal properties of 80 modular MPNs was constructed, and the ML process was optimized through feature engineering and model training. The selected extreme gradient boosting model (XGBoost) successfully identified 1,654 high photothermal MPNs from a virtual database of 44,438. Subsequent experimental validation revealed a remarkable success rate of 70% in predicting high photothermal MPNs. Additionally, several previously unreported high photothermal MPNs were discovered, demonstrating advantages in photothermal antibacterial applications. This study offers an innovative ML-driven approach for the efficient screening of MPN materials, providing a solid foundation for PTA design in PTT and other biomedical applications.

DOI: 10.1002/anie.202423799

Source: https://onlinelibrary.wiley.com/doi/10.1002/anie.202423799