Many approaches have been developed to represent molecules for property prediction, utilizing a variety of molecular representations, such as graphs, strings, precomputed feature vectors like Morgan fingerprint, and atomic coordinate sets. While traditional methods often rely on fixed molecular representations crafted by experts, many recent works have used graph-based ML techniques that generate learned representations. These models can learn two types of mappings: one from the input molecular graph to a latent embedding and one from the latent embedding to the final target property. State-of-the-art deep learning architectures for molecular property prediction, particularly directed message-passing neural networks (D-MPNNs), which are used in the open-source Chemprop software package, have shown success in predicting various chemical properties, including solvation thermodynamics, critical properties, reaction barriers, and infrared spectra, among others.