Computational characterization and machine learning analysis of quantum optimized marine fungal metabolites targeting PD-L1 in cancer immunotherapy

Cancer immune evasion is predominantly mediated through immune checkpoint pathways, such as the PD-1/PD-L1 axis. In this mechanism, PD-L1, which is often overexpressed on tumor cells, binds to PD-1 receptors on T cells, resulting in the inhibition of T cell activity and allowing tumors to evade immune surveillance. Targeting this interaction is of therapeutic significance. Marine fungal metabolites were investigated as potential PD-L1 inhibitors using a multi-level computational approach that combines quantum chemical, dynamic, energetic, and machine learning studies. Preliminary virtual screening narrowed down the list to the top four contenders, CMNPD20987, CMNPD20986, CMNPD24819, and CMNPD20907, with docking values ranging from − 10.7 to − 8.2 kcal/mol. HOMO-LUMO gap analysis based on the density functional theory demonstrated the highest electronic stability of CMNPD24819 (5.087 eV) and the highest reactivity of CMNPD20907 (3.954 eV). Redocking studies highlighted stable interactions with critical PD-L1 amino acid residues like Tyr⁵⁶ (π–π stacking), Asp¹²², Gln⁶⁶, Ile¹¹⁶, and Lys¹²⁴ (hydrogen bonding). Triplicate 200 ns MD simulations established the structural stability of the chosen complexes with low RMSD and RMSF values. MM/GBSA binding free energies estimated significant affinity, with notable affinity for CMNPD24819 (− 34.39 kcal/mol) and CMNPD20987 (− 30.63 kcal/mol). Analysis of free energy landscapes showed deep minima of the free energy basin, indicating stable conformational states. The machine learning regression model trained on ChEMBL PD-L1 inhibitors predicted high pIC50 values for the selected compounds, with CMNPD20907, CMNPD20986, CMNPD20987, and CMNPD24819scoring above the reference molecule. This holistic analysis highlights the electronic strength, beneficial binding profiles, and biomedical value of the marine fungal metabolites as potential future immune checkpoint inhibitors of cancer.