Machine learning in general, and especially deep learning, has become a powerful tool in various fields of chemistry. Applications range from the prediction of physico-chemical and pharmacological properties of molecules to the design of molecules or materials with certain properties, the exploration of chemical synthesis pathways, or the prediction of properties important for chemical analysis like IR, UV/VIS, or mass spectra. Many combinations of molecular representation and model architecture have been developed to extract features from molecules and predict molecular properties. Molecules can be represented as graphs, strings, pre-computed feature vectors, or sets of atomic coordinates and processed using graph-convolutional neural networks, transformers, or feed-forward neural networks to train predictive models. While early works focused on hand-made features or simple fingerprinting methods combined with kernel regression or neural networks, the current state-of-the-art has shifted to end-to-end trainable models which directly learn to extract their own features. Here, the models can achieve extreme complexity based on the mechanisms of information exchange between parts of the molecule. For example, graph convolutional neural networks (GCNNs) extract local information from the molecular graph for single or small groups of atoms, and use that information to update the immediate neighborhood. They offer robust performance for properties dependent on the local structure, and if the three-dimensional conformation of a molecule is not known or not relevant for a prediction task. Graph attention transformers allow for a less local information exchange via attention layers, which learn to accumulate the features of atoms both close and far away in the graph. Another important line of research comprises the prediction of properties dependent on the three-dimensional conformation of a molecule, such as the prediction of properties obtained from quantum mechanics. Finally, transformer models from natural language processing can be trained on string representations such as SMILES or SELFIES, also leading to promising results. In this work, we discuss our application of GCNNs, namely Chemprop, 36 a directed-message passing algorithm derived from the seminal work of Gilmer et al. An early version of Chemprop was published in Ref. 36. Since then, the software has substantially evolved, and now includes a vast collection of new features. For example, Chemprop is now able to predict properties for systems containing multiple molecules, such as solute/solvent combinations, or reactions with and without solvent. It can train on molecular targets, spectra, or atom/bond-level targets, and output the latent representation for analysis of the learned feature embedding. Available uncertainty metrics include popular approaches such as ensembling, mean-variance estimation, and evidential learning. Chemprop is thus a general and versatile deep learning toolbox, and enjoys a wide user base.