2. Academic Validation
  3. Artificial intelligence accelerates the identification of nature-derived potent LOXL2 inhibitors

Artificial intelligence accelerates the identification of nature-derived potent LOXL2 inhibitors

  • Sci Rep. 2025 Mar 27;15(1):10540. doi: 10.1038/s41598-025-95530-9.
Xiaowei Jia # 1,  Meng Liu # 2,  Yushi Tang 1,  Jingyan Meng 1,  Ruolin Fang 1,  Xiting Wang 3,  Cheng Li 4 5
Affiliations

Affiliations

  • 1 School of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China.
  • 2 Sijiqing Hospital, Beijing, China.
  • 3 School of Traditional Chinese Medicine, Beijing University of Chinese Medicine, No.11 Bei San Huan Dong Lu, Beijing, 100029, China. wangxiting@amss.ac.cn.
  • 4 School of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin, China. licheng9311@163.com.
  • 5 Tian Jin Key Laboratory of Modern Chinese Medicine Theory of Innovation and Application, No.10 Poyang Lake Road, Tianjin, 301617, China. licheng9311@163.com.
  • # Contributed equally.
PMID: 40148559 DOI: 10.1038/s41598-025-95530-9
Abstract

The role of LOXL2 in Cancer has been widely demonstrated, but current therapies targeting LOXL2 are not yet fully developed. We believe that selective nature-derived inhibition of LOXL2 may provide a better therapeutic approach for the treatment of Cancer. Therefore, we adopted a comprehensive approach combining deep learning and traditional computer-aided drug design methods to screen LOXL2 selective inhibitors. Bioactivity and affinity of the potential LOXL2 inhibitors were determined by molecular docking and virtual screening. At the same time, we experimentally tested the effect of potential LOXL2 inhibitors on Cancer cells. Validation showed that it could inhibit proliferation and migration, promote Apoptosis of CT26 cells, and reduce the expression level of LOXL2 protein. As a result, we identified a potent LOXL2 inhibitor: the natural product Forsythoside A, and demonstrated that Forsythoside A has an inhibitory effect on tumors.

Keywords

Cancer; Deep learning; Drug discovery; LOXL2.

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