2. Academic Validation
  3. Discovery and optimization of Menin-MLL inhibitors targeting acute myeloid leukemia

Discovery and optimization of Menin-MLL inhibitors targeting acute myeloid leukemia

  • Eur J Med Chem. 2026 Apr 5:307:118641. doi: 10.1016/j.ejmech.2026.118641.
Qitao Xiao 1,  Yuxian Wang 2,  Zheyuan Shen 1,  Jun Mo 1,  Cong Li 3,  Rongkuan Jiang 1,  Jingyu Zhang 1,  Yubo Zhou 4,  Xiaowu Dong 5,  Hanlin Wang 6,  Tao Liu 7
Affiliations

Affiliations

  • 1 Institute of Drug Discovery and Design, College of Pharmaceutical Sciences, Zhejiang University, Zijingang Campus, Hangzhou, 310058, China.
  • 2 School of Pharmacy, Henan University, Kaifeng, 475004, China; State Key Laboratory of Chemical Biology, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China; University of Chinese Academy of Sciences, Beijing, 100049, China.
  • 3 State Key Laboratory of Chemical Biology, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China; University of Chinese Academy of Sciences, Beijing, 100049, China.
  • 4 State Key Laboratory of Chemical Biology, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China; University of Chinese Academy of Sciences, Beijing, 100049, China; Zhongshan Institute for Drug Discovery, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Zhongshan, 528400, China.
  • 5 Institute of Drug Discovery and Design, College of Pharmaceutical Sciences, Zhejiang University, Zijingang Campus, Hangzhou, 310058, China. Electronic address: dongxw@zju.edu.cn.
  • 6 State Key Laboratory of Chemical Biology, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China; University of Chinese Academy of Sciences, Beijing, 100049, China. Electronic address: wanghanlin@simm.ac.cn.
  • 7 Institute of Drug Discovery and Design, College of Pharmaceutical Sciences, Zhejiang University, Zijingang Campus, Hangzhou, 310058, China; Hangzhou Institute of Innovative Medicine, Institute of Drug Discovery and Design, Zhejiang University, Hangzhou, 310058, China. Electronic address: lt601@zju.edu.cn.
PMID: 41666760 DOI: 10.1016/j.ejmech.2026.118641
Abstract

A machine learning-guided strategy, which integrated unsupervised structural clustering to identify diverse scaffolds for molecular hybridization followed by synergistic QSAR and molecular docking screening, identified lead compound 7. Guided by this lead, a series of thieno[2,3-d]pyrimidine derivatives were developed as menin inhibitors through several rounds of rational structural optimization. Among them, compound A13 exhibited potent anti-proliferative activity against MV4-11 cells (0.379 ± 0.182 μM). Besides, mechanistic studies confirmed A13 disrupts menin-MLL interactions, induces cell differentiation, and selectively inhibits MLL-rearranged (MV4-11, MOLM-13) and DNMT3A/NPM1-mutated (OCI-AML3) leukemia cells. The stable binding mode of A13 with menin was further elucidated by molecular dynamics simulations. Moreover, A13 exhibited favorable oral pharmacokinetic properties, characterized by rapid absorption (Tmax = 1.67 h) and high plasma exposure (AUC0-t = 2241 ng h/mL), demonstrating its potential as a promising candidate for further preclinical development against MLL-rearranged AML.

Keywords

MLL-R AML; Machine learning; Menin inhibitor; thieno[2,3-d]pyrimidine.

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