ElixirSeeker: A Machine Learning Framework Utilizing Fusion Molecular Fingerprints for the Discovery of Lifespan-Extending Compounds.

Journal: Aging cell
Published Date: May 26, 2025

Abstract

Despite the growing interest in developing anti-aging drugs, high costs and low success rates of traditional drug discovery methods pose significant challenges. Aging is a complex biological process associated with numerous diseases, making the identification of compounds that can modulate aging mechanisms critically important. Accelerating the discovery of potential anti-aging compounds is essential to overcome these barriers and enhance lifespan and healthspan. Here, we present ElixirSeeker, a machine learning framework designed to maximize feature capture of lifespan-extending compounds through multi-fingerprint fusion mechanisms. Utilizing this approach, we identified several promising candidate drugs from external compound databases. We tested the top six hits in Caenorhabditis elegans and found that four of these compounds-including Praeruptorin C, Polyphyllin VI, Thymoquinone, and Medrysone-extended the organism's lifespan. This study demonstrates that ElixirSeeker effectively accelerates the identification of viable anti-aging compounds, potentially reducing costs and increasing the success rate of drug development in this field.

Authors

  • Yan Pan
    Department of Gastroenterology, Sichuan Academy of Medical Sciences and Sichuan Provincial People's Hospital, Chengdu, China.
  • Hongxia Cai
    Clinical Research Center, The First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang 310003, PR China.
  • Fang Ye
    Bone Marrow Transplantation Center of the First Affiliated Hospital, and Center for Stem Cell and Regenerative Medicine, Zhejiang University School of Medicine, Hangzhou, Zhejiang, 310000, China.
  • Wentao Xu
    Department of Materials Science and Engineering, Pohang University of Science and Technology (POSTECH), Pohang 790-784, Republic of Korea.
  • Zhihang Huang
    Laboratory of Aging Research, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
  • Jingyuan Zhu
    Laboratory of Aging Research, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
  • Yiwen Gong
    Department of Cardiovascular Medicine, Heart Failure Center, Ruijin Hospital Lu Wan Branch, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Yutong Li
    From CT Business Unit, Neusoft Medical System Company, Shenyang, China.
  • Anastasia Ngozi Ezemaduka
    Laboratory of Aging Research, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.
  • Shan Gao
    Department of Mathematics and Statistics, Yunnan University, China.
  • Shunqi Liu
    College of Animal Science and Technology, China Agricultural University, Beijing, China.
  • Guojun Li
    Department of Urology, Xiangya Changde Hospital, 415000 Changde, Hunan, China.
  • Hao Li
    Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Jing Yang
    Beijing Novartis Pharma Co. Ltd., Beijing, China.
  • Junyu Ning
    Institute for Toxicology, Beijing Center for Disease Prevention and Control, Beijing, China.
  • Bo Xian
    Department of Neurology, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, Chengdu, China.

Keywords

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