We have already seen that we can obtain good results independent of how we represent a molecule (IUPAC names, SMILES, or SELFIES), but can GPT-3 interpret an abstract representation of molecules we invented? Jablonka et al. developed a machine learning approach to design dispersants using a coarse-grained approach. This dispersant was a linear copolymer with four monomer types and a chain length between 16 and 48 units, giving a chemical design space of 58 million different dispersants. One important goal in this work was to find dispersants with the right binding free energy, i.e., which polymer length and which monomer sequence is optimal. As there is no way the GPT-3 knows about the properties or representations of the coarse-grained polymers, it is interesting to see if we can get any sensible result if we ask the question What is the adsorption free energy of coarse-grained dispersant AAAABBBBDDDDAAAACCCC or as inverse design, Give me a structure of a coarse-grained dispersant with a free energy of 17. Interestingly, for the prediction of the adsorption free energy, the GPT-3 model outperforms the models developed by Jablonka et al. Additionally, it can also successfully carry out the inverse design and generate monomer sequences that give the desired composition and, with a mean percentage error of around 22 %, the desired adsorption free energy (the approximation of the ground truth we use already has a mean percentage error of around 9 %, see Supplementary Note 12.1 for details).