The prostaglandin E2 receptor EP3 is emerging as a promising therapeutic target in cardiovascular disease due to its involvement in vascular inflammation, platelet aggregation, and vasoconstriction. However, selective EP3 ligands with validated biological activity remain scarce. Here, we combine computational and experimental strategies to discover and validate novel EP3 receptor ligands with therapeutic potential. We implemented a high-throughput, structure- and ligand- based virtual screening pipeline, enabling the efficient exploration of approved drugs and natural compounds from DrugBank and FooDB libraries. Top-scoring candidates were prioritized based on binding energy, and pharmacophoric similarity. Selected hits underwent in silico ADME/Tox profiling using QikProp, identifying molecules with favorable pharmacokinetic and safety parameters. TUCA, masoprocol, and pravastatin sodium emerged as lead candidates and were validated in vitro using endothelial migration and platelet aggregation assays. TUCA exhibited the most consistent inhibitory effect on endothelial migration, while masoprocol, and hydrocortisone significantly reduced platelet aggregation. These findings establish a multidimensional workflow for the rational identification of EP3 ligands and support their potential in cardiovascular therapeutics.

Docking methods have improved significantly with AI optimization and systematic exploration of parameter configurations within docking software. However, docking large molecules like peptides remains challenging. Existing tools struggle with accuracy and efficiency for peptides of larger lengths, and generative AI approaches still lack robust ranking metrics and the performances could be suboptimal. To address this, we developed STELLAR (Score-Tuning for Efficient ranking of Large Ligands using an Accurate and Refined docking configuration), a workflow able to process docking for peptides longer than 10 residues. STELLAR implements a fragment-based blind docking (FBD) strategy, which decomposes peptides into smaller units for individual docking and subsequent recomposition into full-length structures. Built on the MetaScreener platform (https://github.com/bio-hpc/metascreener), STELLAR includes optimized algorithms for handling large peptides. The pipeline also integrates structural optimization steps using tools such as GROMACS and PyMOL, ensuring physically realistic poses. STELLAR achieved low RMSD values in the validation conducted using benchmark complexes from Propedia and PDB. It outperforms several state-of-the-art tools such as HPepDock, DiffDock, and CABS-Dock across a range of peptide lengths (up to 30 residues). It scales linearly in computing time (increasing with every three amino acids), runs efficiently on CPUs, avoids reliance on AI approximations, supports modular docking engines (Gnina, LeadFinder, etc.), and reduces computing time compared to other similar tools. Its design also supports high-throughput screening for peptide-protein interactions by efficiently managing conformational flexibility.