Graph neural networks enable learning both molecular representation and property endto-end directly from the molecular graph. As detailed above in Section 2, the learned node representations are aggregated into a molecule-level representation after the message-passing phase, which we refer to as the "learned fingerprint." This embedding is then further fed into the FFN network. Within the FFN, we consider the final hidden representation, which we refer to as the "ffn embedding". Both of these vectors are latent representations of a molecule, as it relates to a particular trained model. Molecule latent representations can be useful for data clustering or use as additional features in other models. Chemprop supports the calculation of either from a trained model for a given set of molecules.