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 Tyr