All investigated CLMs were trained on 1.9M canonical SMILES strings extracted from ChEMBL v31 . The generated strings were evaluated according to their (a) ). Their designs approximated the training and test sets in terms of selected properties (Figure ). These results agree with the literature on CLMs and demonstrate the robustness of the model training procedure. S4 designs the most valid, unique, and novel molecules, by generating more novel molecules than the benchmarks (from approximately 4,000 to 12,000 more), and displays a good ability to learn the 'chemical syntax' of SMILES strings.