Discovery of a Potent Lysine-Specific Histone Demethylase-1 (LSD-1) Inhibitor for Cancer Treatment via Machine Learning, Molecular Modeling, and In Vitro Validation

  • Chemistry. 2025 Oct 28:e01379. doi: 10.1002/chem.202501379.
Shuya Wang  1  2 Lihui Duo  1 Zhangyi Ma  1 Zixuan Zhang  1  2 Biyu Hu  1 Hainam Do  1 Binjie Hu  1 Michael J Stocks  3 Jianfeng Ren  4 Bruno N Falcone  2 Jonathan D Hirst  2 Guang Yang  5 Bencan Tang  1
Affiliations
  • 1. Department of Chemical and Environmental Engineering, The University of Nottingham Ningbo China, 199 Taikang East Road, Ningbo, 315100, P. R. China.
  • 2. School of Chemistry, University of Nottingham, University Park, Nottingham, NG7 2RD, UK.
  • 3. Nottingham University Biodiscovery Institute, School of Pharmacy, University of Nottingham, University Park, Nottingham, NG7 2RD, UK.
  • 4. School of Computer Science, The University of Nottingham Ningbo China, 199 Taikang East Road, Ningbo, 315100, P. R. China.
  • 5. State Key Laboratory of Medicinal Chemical Biology, College of Pharmacy, Nankai University, Tianjin, 300071, P. R. China.
Abstract

The discovery of potent lysine-specific Histone Demethylase 1 (LSD-1) inhibitors is of significant importance in Cancer therapy, as LSD-1 overexpression contributes to tumour progression and malignancy through the regulation of gene expression. We employed an integrated strategy that combined machine learning-based virtual screening, bioassay validation, and ligand-target interaction analysis. Virtual screening identified 29 candidate compounds, of which 17 demonstrated micromolar-level enzymatic inhibition in experimental assays. Among them, compound L01 showed strong potential as an LSD-1 inhibitor, exhibiting significant antileukemic activity (IC50 = 24 µM) and confirmed binding to LSD-1 in Kasumi-1 cells. MST and SPR analysis confirmed the direct binding between L01 and LSD-1 inferred from our in vitro studies. Molecular docking and molecular dynamics simulations further supported the interaction, revealing a binding affinity of L01 comparable to that of the well-characterised reversible inhibitor SP-2577. ADMET analysis revealed that L01 possesses excellent Caco-2 permeability and high target specificity; however, its moderate drug-likeness and potential toxicity issues underscore the need for further optimisation. Through computational modelling and experimental validation, our study identifies L01 as a promising LSD-1 inhibitor with strong potential for optimisation to improve bioavailability and safety in Cancer therapy.

Keywords
bioassay validation; lysine‐specific histone demethylase 1 inhibitors; machine learning‐based virtual screening; molecular docking; molecular dynamics simulation.
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