The method has a few limitations especially related to the contribution of water-water interactions, which we aim at addressing in future versions. The availability of a rapid method to perform these calculations and characterize water networks can help revealing otherwise hidden patterns that can be valuable for drug design. Also, the analysis of these results will be used for identifying current limitations of the method that will need to be tackle in future versions. The method we presented is able to capture with satisfactory accuracy higher levels of water molecule interactions beyond the first hydration shell, providing valuable insight for the design of inhibitors. Implementation/Availability. WaterKit is entirely written in Python 167 , and relies on the scientific Python stack composed of NumPy 168 , SciPy and Pandas 169 to store, process, and analyze data efficiently. The module multiprocessing is also used to parallelize the simulation on multiple cores. The library OpenBabel 170 is used to read the input PDBQT file, identify the different APs using SMART patterns and build the HBVs using the chemical perception feature. Amber-Tools 19 toolkit is used to prepare the protein structure and estimate the desolvation free energy with GIST implemented in CPPtraj. The water molecule ensembles is energy minimized with OpenMM. The library parmed is used to help convert files between different file formats and concatenate the water ensemble as a trajectory. Detailed documentation and source code of WaterKit is accessible under the GPL license at .