2. Academic Validation
  3. Identification of a novel Aurora B inhibitor using the AI-driven drug screening and docking-based traditional screening

Identification of a novel Aurora B inhibitor using the AI-driven drug screening and docking-based traditional screening

  • Bioorg Med Chem. 2025 Sep 27:131:118423. doi: 10.1016/j.bmc.2025.118423.
Jiayuan Ye 1,  Nan Chen 1,  Yixiang Zhu 2,  Yana Xu 3,  Chenghao Pan 4,  Yaojiang Xu 5
Affiliations

Affiliations

  • 1 Department of Hepatology, Shangyu People's Hospital of Shaoxing, Shaoxing University, Shaoxing, Zhejiang Province, 312399, China.
  • 2 Department of Neurology, The First Affiliated Hospital of Soochow University, Suzhou 215006, Jiangsu Province, China.
  • 3 Hangzhou First People's Hospital Chengbei Campus, Hangzhou 310058, PR China.
  • 4 College of Pharmaceutical Sciences, Zhejiang University, Hangzhou 310058, PR China.
  • 5 Department of Hepatology, Shangyu People's Hospital of Shaoxing, Shaoxing University, Shaoxing, Zhejiang Province, 312399, China. Electronic address: idsa_xyj@163.com.
PMID: 41033076 DOI: 10.1016/j.bmc.2025.118423
Abstract

Aurora B, a subtype of Aurora kinases that functions as a serine/threonine kinase, playing a vital role in the process of Mitosis, is often overexpressed in certain tumor cells leading to tumorigenesis and progression. Therefore, the development of small molecule inhibitors targeting Aurora B holds promise for providing new options for some Cancer patients. In this study, we efficiently screened 4 compounds from MCE compound database using a combination of machine learning-based screening and structure-based screening. The results showed that 2 compounds exhibited strong Aurora B inhibitory activity in a homogeneous time-resolved fluorescence (HTRF) assay, indicating a high hit rate for this screening method. Among them, compound 4 demonstrated optimal inhibitory activity against Aurora B, with an IC50 value of 15.54 nM, comparable to Aurora B inhibitors that have entered clinical trials. In vitro experiments indicated that compound 4 effectively inhibited Huh-7 and Huh-6 cells, with IC50 values of 0.9 μM and 1.8 μM, respectively. Molecular dynamics simulation results revealed that the compound binds to the ATP binding pocket of Aurora B, forming hydrogen bond interactions with Glu171 and Glu220, salt bridges with Asp234 and Glu177, and a pi-cation interaction with Arg97. In summary, by integrating multi-modal screening approaches, we successfully identified a potent Aurora B Inhibitor with in vitro antitumor activity, providing lead compounds for subsequent drug development.

Keywords

Aurora B virtual screening machine learning molecular dynamic HCC.

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