AI-guided integrative discovery of PD-1/PD-L1 interface inhibitors through multiscale modeling and experimental validation.

The PD-1/PD-L1 protein-protein interaction (PPI) is a critical immune checkpoint, and its inhibition represents a powerful strategy in oncology. Disrupting this macromolecular complex with small molecules remains a significant challenge. This study establishes a comprehensive pipeline for the discovery of novel in stock PD-1/PD-L1 inhibitors. We first developed a robust machine learning-based quantitative structure-activity relationship (ML-QSAR) model to screen chemical libraries virtually. Top-ranking hits were subjected to molecular docking against the PD-L1 dimer interface to evaluate potential binding modes. Subsequently, extensive molecular dynamics (MD) simulations provided critical insights into the structural stability and dynamic interactions at the macromolecular interface, revealing the compounds' mechanism of complex disruption. The most promising candidate, designated PDA13, was advanced to in vitro validation, demonstrating direct binding to the PD-L1 protein and effectively inhibiting the PD-1/PD-L1 interaction with an IC of hit 17.53 μM. Our work underscores the synergy of computational and experimental strategies in targeting PPI of PD-L1 dimer. The identified PDA13 scaffold provides a valuable starting point for the development of novel immunotherapeutic agents, and the detailed biophysical and structural insights into its mechanism of action form the core of this contribution.