Another way to overcome order dependence in learned chemical space was proposed by Winter et al. The authors used a graph-based autoencoder to create a latent space without any influence of atoms' order, where the encoder consisted only of graph convolution networks. The obtained latent vectors were tested on four Quantitative Structure-Property Relationship (QSPR) tasks using MoleculeNet benchmarking . The obtained results have demonstrated slight outperforming of learned molecular representations in comparison to ECFP fragments.