Integrated Machine Learning and Molecular Simulation-Guided Discovery of Novel Small-Molecule PD-L1 Inhibitors

Abstract

Background/Objectives: The programmed death-1/programmed death-ligand 1 (PD-1/PD-L1) immune checkpoint is a key therapeutic target in cancer immunotherapy, but small-molecule inhibition remains challenging due to its shallow and dynamic interaction interface. This study aimed to develop an artificial intelligence (AI)-guided workflow to identify novel small-molecule inhibitors targeting the PD-L1 dimer interface. Methods: A combined computational and experimental approach was established. A support vector regression-genetic algorithm (SVR-GA) model was trained on a dataset of 1385 known PD-L1 inhibitors to predict activity and guide molecular generation. From 470 AI-generated candidates, docking and molecular dynamics (MD) simulations were used for virtual screening. Selected compounds were synthesized and evaluated for PD-1/PD-L1 binding disruption using homogeneous time-resolved fluorescence (HTRF) assays. Cytotoxicity was tested in MDA-MB-231 and 4T1 cell monocultures, and in vivo efficacy was assessed in an immunocompetent 4T1 tumor model. Results: Two hits, PD-L1-Ser and PD-L1-Ser-OEt, were identified. Both disrupted PD-1/PD-L1 binding in HTRF assays, with PD-L1-Ser-OEt showing higher potency (IC50 = 0.2068 μM). Both compounds exhibited limited direct cytotoxicity in cancer cell monocultures, suggesting an immune-mediated mechanism. In the 4T1 syngeneic mouse model, both inhibitors suppressed tumor growth without causing body weight loss. PD-L1-Ser-OEt demonstrated superior antitumor efficacy and elevated serum levels of IFN-γ and IL-4. Conclusions: This AI-guided workflow combining machine-learning-based molecular generation with structure validation is feasible for discovering PD-L1 dimer-interface inhibitors. PD-L1-Ser-OEt represents a promising lead compound for further development as an immune checkpoint inhibitor.