Recent research shows that the atomic descriptors introduced by Finkelmann et al. , using charge model 5 (CM5) atomic charges , is an excellent representation of atoms in molecules as the feature descriptor for ML models to predict various properties. These properties encompass the site of metabolism , the strengths of hydrogen bond donors and acceptors , the regioselectivity of electrophilic aromatic substitution reactions , and C-H pK a values . Building on the methodology from Finkelmann et al. , Ree et al. and our previous work , 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. We have updated the sorting algorithm to sort the CM5 charges according to the Cahn-Ingold Prelog rules. Furthermore, we use a shell radius of 3 to describe the local environment better and speed up the training of our ML model, but at the cost of a slight decrease in accuracy. Refer to section 4 in the Supporting Information for more details.