We investigate seven different atomic descriptors as input to the ML models (details on the descriptors are given in Table in the supporting information). The atomic descriptors are developed by Finkelmann et al. and have been successfully applied to the prediction of site of metabolism, hydrogen bond donor and acceptor strengths, and Ames mutagenicity of primary aromatic amines. Almost all of the descriptors depend on charge model 5 (CM5) atomic charges, which are obtained from a single point calculation using GFN1-xTB as implemented in the open source semiempirical software package xtb version 6.4.0. 26 This particular charge scheme has been shown to be largely conformation-independent and to correctly reflect changes in the chemical environment i.e. substituents effects. Hence, only a single conformer is generated for each molecule using ETKDG versions 3 27 with us-eSmallRingTorsions=True as implemented in RDKit version 2020.09.4. This is the key to using quantum chemical derived descriptors as the computational cost is kept at a minimum (details about computational timings are provided in Results and discussion). The atomic descriptors are generated fully automatically from a SMILES representation of a given molecule.