DockM8 currently supports five distinct docking algorithms: (1) SMINA, a fork of AutoDock Vina with an improved scoring function and energy minimization processes . (2) GNINA, an offshoot of SMINA, which employs convolutional neural networks (CNN) as a scoring function . (3) QVINA-W and (4) QVINA2 , both derived from AutoDock Vina , which introduce advancements in computational speed, thereby expediting the docking process. And (5) PLANTS, which is based on an ant colony optimization algorithm . The user can choose from any combination of the above mentioned docking algorithms integrated in DockM8. In all cases, the docking calculations are parallelized by running one docking run on one CPU core and using the multiprocessing or joblib libraries to manage the running jobs. The ligand library is split beforehand according to the number of CPU cores defined by the user to allow parallel handling of the docking tasks. For QVINA-W and QVINA2, the necessary conversion of the ligand and receptor files to pdbqt-files is handled by Meeko and the Python implementation of OpenBabel with the addition of Gasteiger charges , respectively.