One group of deep-learning approaches consists of architectures learning molecular vectors in unsupervised manners (e.g., autoencoders, normalising flows, etc.). They apply various operations to transform high-dimensional input into low-dimensional latent vectors. However, some of these operations have undesirable effects. The influence of atom order on the molecular latent vector can be an example of such an impact. It was first shown for SMILES-based architectures. SMILES-based architectures use sequential operations, which rely on the arrangement of the input symbols. Atoms' order influences the latent space of such architectures. This allows researchers to achieve higher reconstruction rates. At the same time, it damages the topology of the latent space. Bjerrum et al. studied the order-independence of latent vectors produced by a SMILES-based heteroencoder. The heteroencoder was trained using SMILES data augmentation with different atom orders. It was shown that the latent vectors of the heteroencoder improve performance in the Quantitative Structure-Activity Relationship (QSAR) task compared to an autoencoder trained on canonical SMILES. Higher performance was explained by removing atoms' order dependence. Sadly, the reconstruction ability of such a heteroencoder decreased dramatically compared to an autoencoder based on canonical SMILES.