Drug Repurposing for AML: Structure-Based Virtual Screening and Molecular Simulations of FDA-Approved Compounds with...

Background: Acute myeloid leukemia (AML) is a heterogeneous hematologic malignancy characterized by impaired differentiation, apoptosis resistance, and metabolic reprogramming, which collectively contribute to therapeutic resistance and poor clinical outcomes. While targeted agents-such as LSD1 inhibitors, the BCL-2 inhibitor venetoclax, and IDH1 inhibitors-have provided clinical benefit, their efficacy is often limited by compensatory signaling and clonal evolution. This study aimed to identify FDA-approved compounds with multitarget potential to simultaneously modulate key epigenetic, apoptotic, and metabolic pathways in AML. Methods: Structure-based virtual screening of 3957 FDA-approved molecules was performed against three AML-relevant targets: lysine-specific demethylase 1 (LSD1), BCL-2, and mutant IDH1 (R132H). Top-ranked hits were evaluated using ADMET prediction and molecular dynamics (MD) simulations to assess pharmacokinetic properties, toxicity, and ligand-protein complex stability over 100 ns trajectories. Results: Three compounds-DB16703, DB08512, and DB16047-exhibited high binding affinities across all three targets with favorable pharmacokinetic and safety profiles. MD simulations confirmed the structural stability of the ligand-protein complexes, revealing persistent hydrogen bonding and minimal conformational deviation. These findings suggest that these repurposed drugs possess a promising multitarget profile capable of addressing AML's multifactorial pathophysiology. Conclusions: This computational study supports the feasibility of a polypharmacology-based strategy for AML therapy by integrating epigenetic modulation, apoptotic reactivation, and metabolic correction within single molecular scaffolds. However, the identified compounds (Belumosudil, DB08512, and Elraglusib) have not yet demonstrated efficacy in AML models; further preclinical validation is warranted to substantiate these predictions and advance translational development.