Discovery of novel MDM2 inhibitors from a Penicillium metabolome library: An integrated phylogenetic, machine...

Restoring the tumor-suppressor function of p53 by inhibiting its negative regulator, MDM2, represents a significant therapeutic avenue for cancers that maintain wild-type p53. This research aimed to identify new MDM2 inhibitors through a phylogenetically guided strategy that involved the construction of a focused virtual library of metabolites derived from the Penicillium genus. A comprehensive computational framework was developed, employing machine learning-based quantitative structure-activity relationship (ML-QSAR) modeling, ensemble molecular docking, network pharmacology, molecular dynamics (MD) simulations, and ADMET profiling. The gradient boosting ML-QSAR model achieved a test set R² of 0.80 and was externally validated against 39 known MDM2 inhibitors (R² = 0.82, RMSE = 0.80 pIC₅₀ units), confirming its predictive reliability. Ensemble docking studies against 13 conformations of MDM2 highlighted three leading candidates (CNP0147553.1, CNP0154476.3, and CNP0154476.4) demonstrating binding affinities comparable to the known control inhibitor Nutlin-3a, with docking scores validated against experimental binding data. Further investigations through 500 ns MD simulations provided insights into the stability of the CNP0147553.1-MDM2 complex, which maintained a mean ligand RMSD of 0.039 nm and a complex RMSD of 0.176 nm, alongside a favorable binding free energy of -25.82 kcal/mol. Key residue analysis revealed that CNP0147553.1 achieved pronounced stabilization of critical binding pocket residues, including an 81.6% reduction in flexibility of HIS96. Network pharmacology analysis revealed a polypharmacology potential, indicating that the hub genes related to the identified compounds predominantly converged on the PI3K-AKT-mTOR and RAS-RAF-MAPK signaling pathways. ADMET profiling suggested promising pharmacokinetic and safety profiles for the lead candidates, establishing the basis for future experimental validation.