The article introduces PLIP, a novel web service and command-line tool for fully automated detection and visualization of non-covalent protein–ligand interactions in 3D structures. PLIP is designed to address the lack of comprehensive tools for characterizing interactions in protein–ligand complexes, which are crucial for structural bioinformatics, drug discovery, and biology research. The tool accepts Protein Data Bank (PDB) structures, protein or ligand names, or custom protein–ligand complexes as input and returns a list of detected interactions on a single atom level, covering seven interaction types: hydrogen bonds, hydrophobic contacts, pi-stacking, pi-cation interactions, salt bridges, water bridges, and halogen bonds. PLIP offers publication-ready images, PyMOL session files, and parsable result files for further data processing. The full Python source code is available for download, and the tool supports high-throughput interaction profiling through its command-line mode. The article includes examples of using PLIP for docking post-processing and inhibitor design, demonstrating its utility in evaluating docking results and identifying key residues in inhibitor design.The article introduces PLIP, a novel web service and command-line tool for fully automated detection and visualization of non-covalent protein–ligand interactions in 3D structures. PLIP is designed to address the lack of comprehensive tools for characterizing interactions in protein–ligand complexes, which are crucial for structural bioinformatics, drug discovery, and biology research. The tool accepts Protein Data Bank (PDB) structures, protein or ligand names, or custom protein–ligand complexes as input and returns a list of detected interactions on a single atom level, covering seven interaction types: hydrogen bonds, hydrophobic contacts, pi-stacking, pi-cation interactions, salt bridges, water bridges, and halogen bonds. PLIP offers publication-ready images, PyMOL session files, and parsable result files for further data processing. The full Python source code is available for download, and the tool supports high-throughput interaction profiling through its command-line mode. The article includes examples of using PLIP for docking post-processing and inhibitor design, demonstrating its utility in evaluating docking results and identifying key residues in inhibitor design.