Computational Identification of Marine Natural Products as Potential MDM2 Inhibitors Using E‐Pharmacophore‐Based Virtual Screening, MD Simulations, MM‐GBSA, DFT, PCA, and FEL Analysis

Abstract Cancer is a complex and life‐threatening disease characterized by uncontrolled tumor cell growth. Targeting the MDM2 protein to reactivate the tumor suppressor p53 presents a promising therapeutic strategy. This study employed an energy‐optimized pharmacophore model to perform a virtual screening of 47,451 compounds from the comprehensive marine natural product database (CMNPD), leveraging the rich diversity of marine‐derived bioactives with high efficacy and low side effects. The screening workflow combined ADMET profiling and molecular docking using HTVS, SP and XP methods to identify potential MDM2 inhibitors. Six top hits exhibited strong binding affinities and favorable interactions compared to standard drugs. Molecular dynamics (MD) simulations and density function theory (DFT) studies were conducted to evaluate the structural stability and electronic properties of the protein–ligand complexes. MM‐GBSA calculations quantified the binding free energies, confirming the stability of these complexes. Specifically, comp‐4 and 6 showed low energy gaps in DFT analysis, indicating strong ligand affinity. Principal component analysis (PCA) and free energy landscape (FEL) evaluations further demonstrated stable conformational behavior throughout the MD simulation trajectories. Collectively, these integrated computational approaches identified potent and stable MDM2 inhibitors, providing promising candidates for experimental validation and further development as anticancer therapeutics.