This paper is organized as follows. In the Methodology Section, we first recapitulate the training of our pretrained GNN model on electronic energy, and our transfer learning models to other properties ( 1 H-NMR and 13 C-NMR). Details of the hyperparameters chosen for each architecture and the dataset used for training can all be found in Section 1.1. Following this, in Section 1.2 we discuss how we prepare our reaction datasets from the QM9 dataset using an algorithm that can query any class of reactants and transform them to products via a specified reaction. This dataset creation procedure will serve our observations on chemical reaction analogies in the embedding space which we present in Sections 2.1-2.3. Following our observations, we deduce a replicate model of the embedding space based on layered atomic neighborhood information in Section 2.4 which will explain our chemical analogy observations and reveal the global framework of the embedding space. Lastly, in Section 2.5 we show how linear analogies can reveal hidden relations in chemical properties such as 1 H-NMR and 13 C-NMR.