Adapting from the work of , we use the variational auto-encoder neural network to build our model. To this end, we train modified message passing networks to encode the molecular graph and a GRU-based message passing network to decode it. This approach produces mappings from the space of molecules to the embedding vector space and back, allowing us to later train the regression model from the embedding space and determine the molecular property of interest (see Fig. ).