A dataset mixture to train the encoder-decoder pair. Originally in , JT VAE was studied by utilizing the ZINC dataset . It was discovered that ZINC lacks a variety of molecule types to the extent that the models trained on it lack efficiency in real tasks . To avoid the influence of a bias of a particular dataset on the training, we have extended the previous approach to a mixture of QM9 and ZINC datasets (we call it MIX) which helped us improve the model's generalization properties and general accuracy of reconstruction.