During the analysis of the HyFactor architecture, it was discovered that the latent vectors of the autoencoder depend on the canonical ordering of atoms. Since its encoder includes a recurrent neural network (RNN) in the graph aggregation step, the neighbourhood behaviour in the latent space also depends on the order of atoms. In contrast, the results demonstrate that autoencoder of LatentGAN does not suffer from this problem, as it uses a heteroencoder with the SMILES augmentation technique.