These SMILES sets can be used to train baseline ML models serving as a reference for more elaborate methods. We show that using our SMILES to obtain fingerprints or graphs and then training ML models on these representations, we are able to predict polarizability and HOMO-LUMO gap to the same level of accuracy as Kneiding et al. . As such, the SMILES graphs are sufficient for this task, avoiding the need for expensive DFT-NBO calculations. The lack of 3D information in the SMILES hinders the accurate prediction of the dipole moment and we are therefore not able to match the performance of the DFT-NBO data. This performance and the fact that the SMILES are not filtered for inaccurate representations, combined with the ease of which the SMILES can be obtained with the Hückel method, illustrates how powerful these representations can be for TMC development.