The previous benchmark employed an adaptative learning rate strategy (see Suppl. Methods) that uses the UC-JSD metric to evaluate the quality of the models and trigger a learning rate change. Moreover, the same metric was used to select the best epochs to perform a sample for each model. Plotting the UC-JSD against UCC shows a strong correlation in all three SMILES variants (Figure ). It is important to notice that the UC-JSD values should not be compared between models, as the output domain is different. This result shows that it is not necessary anymore to sample all models, but only the one that has the best UC-JSD. That is why for all future benchmarks only the model with the lowest UC-JSD is sampled. Moreover, the GRU cells have not shown any improvement whatsoever compared to the LSTM cells (Suppl. Figure ) and the unrestricted randomized SMILES variant performs worse than the restricted variant. Henceforth, only the restricted variant of randomized SMILES and LSTM cells will be used for the next benchmarks.