Finally, the hardest task for the models is the dipole moment. Again, the models struggle to extrapolate to the structures in the test set. Here, the Gilmer and GCNN models struggle to learn the dipole moment from the SMILES graphs which is due to the fact that the dipole moment is instrisically a geometry dependent property. Since the Baseline, u-NatQ, and d-NatQ models include bond distances it enables the model to learn the correlation between geometric bond information and the dipole moment. For comparison, the baseline model without bond distances as edge features get an R 2 value of 0.42 (this value was kindly offered by the authors). This is something that one can not obtain from only the 2D graphs defined by the SMILES strings. Note that preliminary results predicting dipole moments for organic molecules using ECFP4 showed equally bad results.