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.
| Original language | English |
|---|---|
| Article number | e01379 |
| Journal | Chemistry - A European Journal |
| Volume | 31 |
| Issue number | 67 |
| DOIs | |
| Publication status | Published - 1 Dec 2025 |
Keywords
- bioassay validation
- lysine-specific histone demethylase 1 inhibitors
- machine learning-based virtual screening
- molecular docking
- molecular dynamics simulation
ASJC Scopus subject areas
- Catalysis
- General Chemistry
- Organic Chemistry