To shed additional light on the strengths and limitations of S4 in comparison with the benchmarks, we analyzed the sources of invalid molecule generation for all methods in terms of branching and ring errors, erroneous bond assignment, and other (miscellaneous) syntax issues (Figure ). Interestingly, each method seems to show different types of errors leading to SMILES invalidity. LSTM struggles the most with branching, and performs the best with bond assignment, while GPT struggles the most with rings and bond assignment, and has intermediate performance otherwise. S4 struggles more than LSTM with bond assignment, and generates remarkably fewer errors than both benchmarks in branching and ring design. Our hypothesis is that bond assignment indicates good learning of 'short-range' dependencies, while branching and ring opening and closure require better capturing of the 'long-range' relationships. This suggests that S4 captures long-distance relationships well, in agreement with existing evidence in other domains .