All three metrics, uniformity, completeness and closedness are much higher and show that the restricted randomized models are theoretically able to generate most of GDB-13 with uniform probability. This can be further seen in Figure , where the NLL distribution of a sample of molecules from the GDB-13 randomized SMILES models is centered at 𝑁𝐿𝐿 𝐺𝐷𝐵13 = -𝑙𝑛( Comparing the two variants of randomized SMILES, models trained with the restricted variant have a similarly uniform output domain (Figure ), but models trained with restricted randomized variant have a more complete and more closed domain than those trained with the unrestricted variant. The output domain of the ideal randomized SMILES models would comprise all possible SMILES strings of any given variant possible to be generated from all molecules in GDB-13. This contrasts with the canonical model, in which the output domain is one SMILES per molecule. Each molecule has a different number of SMILES strings, depending on its topology, although only a few (generally highly cyclic or branched molecules) have numbers above 1000 (Figure ). Knowing that the training objective is to obtain a uniform posterior distribution, it would be expected that molecules with more randomized SMILES should have a higher probability of being sampled than those that have fewer. However, this is never the case as models trained with randomized SMILES have a much more uniform posterior probability distribution than those trained with canonical SMILES (Figure ). The model naturally learns to prioritize some SMILES in molecules with a large number of possible SMILES, and to have a more uniform distribution among all possible SMILES on molecules that have less. This can be seen in Figure , where two molecules have the same NLL, but one (blue) has six times the number of possible SMILES than the other (orange). The hyperparameter optimization also gives some hints on how dropout, batch size and cell type affect the training process, although it varies for each SMILES variant. Plots for each hyperparameter compared to the four ratios and the training time were drawn (Suppl. Figure ) and show that adding dropout only makes canonical SMILES models better. The model improves its completeness, but at the expense of closedness, meaning that it generates more molecules from GDB-13 at the expense of making more mistakes. On the other hand, larger batch sizes have generally a positive impact in models of all SMILES variants and at the same time make training processes much faster. But the most interesting result is that the best models for all SMILES variants use LSTM cells. Moreover, even though the training time per epoch of the GRU cells is lower, LSTM models are able to converge with many fewer epochs.