The first task was to generate a foundational model whose latent space could digitize a molecule for subsequent downstream finetuning. As the CCDC dataset is centered around crystal structure data, whose focus is on the geometry of the molecule(s), I opted for a graph-based model. Graph-based models represent molecules as mathematic graphs, interpreting atoms as abstract objects which contain information about their corresponding atoms as nodes: entities which store information, and bonds as edges: entities which represent relationships between nodes. It is of note that the information stored within the atom-nodes is completely user defined and may be as sparse or as dense as the user wishes. The specific graph neural network utilized was a message passing neural network (MPNN), a flavor of graph convolutional neural network which have noted success in a variety of chemistry prediction tasks. Briefly, an MPNN deduces the local chemical environment for each atom within the molecule, preserving the symmetry of chemically identical atoms. The training set for this initial task was comprised of carbon-containing crystal structures in the CCDC. Molecules which contained "rare" atoms (atoms that were represented fewer than 100 times in the dataset) were excluded, which still yielded a broad atomic scope (Figure ). Additionally, structures whose bonding pattern was ambiguous and conformational polymorphs with a significant difference in their conformations were also removed. A large message passing neural network was trained to accept 2D-data of a molecular structure and predict an atomic coordinate proxy of each atom within the molecule. Atomic coordinates are not unique to a molecule or conformation (a molecule rotated through space is the same molecule, but its 3D coordinates will have changed), thus the model predicted the through-space distances of an atom to its nearest neighbors and the corresponding bond angles formed. It was discovered that such an MPNN could accurately predict the bond lengths and angles of unseen molecules (scaffold split) (Table ). Thus, the investigation into the transferability of the foundational model's latent space commenced. It should be noted that crystal structure data has been used to predict solid form and crystal structure based properties but has seen limited application in transfer learning to more tangential tasks. Given the potential foundational chemistry knowledge that could be extracted from the CCDC dataset, it was hypothesized a transfer learning approach from CCDC data would traverse an unexplored gap within the data-driven chemistry literature.