Graph neural networks (GNNs) are a type of neural network (NN) that operate using graph representations of chemical structures. As discussed in Section 2.1, a chemical structure can be defined in terms of nodes and edges, where nodes correspond to atoms and edges correspond to chemical bonds, and descriptors can be assigned to each component to provide a more detailed molecular representation. This graph structure is then passed through a NN composed of layers of neurons with adjustable weights. Through seeing a corpus of training data, the model learns the relationship between the input structures and the target data.