Deep learning-enhanced development of innovative antioxidant liposomal drug delivery systems from natural herbs.

Journal: Materials horizons
Published Date: Jul 2, 2025

Abstract

Free radical-mediated oxidative damage to biological macromolecules, such as DNA and proteins, significantly contributes to cellular ageing. Antioxidants play a crucial role in mitigating this process by neutralizing reactive oxygen species (ROS) and reducing DNA damage. Traditional herbal medicines are of strong interest as potential sources of antioxidants due to their rich diversity of bioactive components. In this study, we developed a two-stage BERT-based framework trained on 587 experimentally confirmed antioxidants and 983 inactive compounds. The optimized model effectively screened a broad range of potential antioxidant compounds from a library of 2882 natural herbal compounds, achieving an accuracy improvement of approximately 20% over traditional machine learning models. Molecular docking simulations and experiments consistently validated the antioxidant capacity of the selected compounds. Additionally, incorporating three representative compounds into a liposomal delivery system not only enhanced bioavailability, but also mitigated oxidative stress injury after kidney acute ischemia/reperfusion. This was achieved by up-regulating antioxidant-related genes in target organs as well as ROS scavenging. Our findings highlight the potential of integrating deep learning-based compound screening with an engineered liposomal delivery platform in the research of oxidative stress and aging.

Authors

  • Xiaohe Zhang
    Bioscience and Biomedical Engineering Thrust, The Hong Kong University of Science and Technology (Guangzhou), Nansha, Guangzhou 511400, China. junwuhkust@ust.hk.
  • Zhihang Zheng
    National Clinical Research Center for Infectious Disease, Shenzhen Third Peoples Hospital, Second Hospital Affiliated to Southern University of Science and Technology, Shenzhen, China.
  • Lina Xie
    Department of Hematology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen 518107, China. zhangzhen1@sysush.com.
  • Minghao Yang
    Thrust of Artificial Intelligence, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, 511466, China. Electronic address: myang272@connect.hkust-gz.edu.cn.
  • Jing Wang
    Endoscopy Center, Peking University Cancer Hospital and Institute, Beijing, China.
  • Weiwei Wang
  • Shuyan Han
    Department of Integration of Chinese and Western Medicine, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Peking University Cancer Hospital and Institute, Beijing, 100142, China. Electronic address: shuyanhan@bjmu.edu.cn.
  • Zhen Zhang
    School of Pharmacy, Jining Medical University, Rizhao, Shandong, China.
  • Jun Wu
    Department of Emergency, Zhuhai Integrated Traditional Chinese and Western Medicine Hospital, Zhuhai, 519020, Guangdong Province, China. quanshabai43@163.com.

Keywords

No keywords available for this article.

External Resources

Popular Topics
Recent Journals