In addition, the embeddings generated by GNNs can be fed into other model architectures. To generate the graph embeddings, the GNN is trained to predict a relevant set of properties. During training, the GNN generates a learned representation (latent space) that captures information about the relationship between molecular structure and the target property. This representation, which is extracted from the embedding layer of the network, can then be used by another ML model to predict a separate target. Figure 2. Schematic representation of different structural encodings of the common donor polymer PTB7-Th.