Recent research shows that the atomic descriptors introduced by Finkelmann et al., using charge model 5 (CM5) atomic charges, is a great representation of atoms in molecules that can be used in combination with an ML model to predict a variety of properties. These properties encompass the site of metabolism, the strengths of hydrogen bond donors and acceptors and the regioselectivity of electrophilic aromatic substitution reactions. Building on the methodology from Finkelmann et al. and Ree et al., we utilize the automated approach to compute CM5 atomic charges from semiempirical tight-binding (GFN1-xTB ) calculations. We modify the workflow to enhance the accuracy of the computed CM5 atomic charges. Instead of generating a single random conformer, we produce 20 random conformers from a SMILES string and optimize the structure with molecular mechanics force fields (MMFF) using RDKit. The CM5 atomic charges of the lowest energy conformer are then used to generate atomic descriptors based on sorting the CM5 charges for a given atom of the input SMILES string. Furthermore, we adjust the shell radius from 5 to 6, improving the performance of the ML model to predict pK a values as detailed in SI -The Descriptor section.