To gain insights into the errors that de novo generators make, 1 million sequences were generated and analyzed. For the pretrained and target-directed RNN models, 5.7 and 4.4 % of the generated sequences were invalid, respectively. The percentage of invalid sequences was slightly higher for the GAN (9.5 %), whereas the VAE had the highest percentage of invalid outputs (88.9 %). A very similar validity of RNN-based models was also found in previous benchmarks . Specific results on the RNN-based model that was used (DrugEx) have also shown that fine-tuning and reinforcement learning slightly increase validity . The validity of the GAN that was used is consistent with the one reported in the MOSES benchmark . It should, however, be noted, that the validity of ORGANIC has been found to be highly variable depending on the training set . This lower validity of VAE outputs is consistent with other studies and is likely caused by regions in the latent space, which are far removed from the training data .