Submission ClassicalGSG DB3 is an empirical method that employed neural networks (NNs) where the inputs are molecular features generated using a method called Geometric Scattering for Graphs (GSG) . In GSG, atomic features are transformed into molecular features using the graph molecular structure. Method COSMO-RS was a QM-based physical prediction approach .. First, this approach used COSMOquick [103] to generate tautomers and discarded irrelevant states due to an internal energy threshold implemented in COSMOquick. The participants conducted a conformational search of every microstate with COSMOconf [104] using up to 150 conformers. Second, for each conformer they performed a geometry optimization using the BP86 functional with a TZVP basis set and the COSMO solvation scheme, followed by a single point energy calculation using the BP86 functional with a def2-TZVPD basis set and the FINE COSMO cavity. All density functional theory calculations were carried out with the TURBOMOLE 7.5 program package . Third, a conformer selection was done by applying COSMOconf (using internally COSMOtherm) to reduce the number of conformers and tautomers for the neutral molecule sets. The final set of the neutral state contained only those conformers and states that are relevant in liquid solutions. Fourth, the COSMOtherm software (version 2020) was used to calculate the free energy difference for each molecule set (from the second step described here) and to calculate the relative weight of the microstates in water. All free energy calculations were carried out using the BP-TZVPD-FINE 20 level of COSMO-RS in COSMOtherm. Within the used COSMO-RS, an ensemble of conformers and microstates is automatically used and weighted according to the total free energy in the respective liquid phase, i.e. different weights are used in water and octanol.