to the software since its initial release and study, 36 including the support of multi-molecule properties, reactions, atom/bond-level properties, and spectra. Additionally, several stateof-the-art approaches to estimate the uncertainty of predictions have been incorporated, as well as pretraining and transfer learning procedures. Furthermore, the code now offers a variety of customization options, such as custom atom and bond features, a large variety of loss functions, and saving the learned feature embeddings for subsequent use with different algorithms. We have showcased and benchmarked Chemprop on a variety of example tasks and datasets, and have found competitive performances for molecular property prediction compared to other approaches available on public leaderboards. In summary, Chemprop is a powerful, fast and convenient tool to learn conformation-independent properties of molecules, sets of molecules, or reactions. which takes the previously generated test set, computes predictions using all models in the checkpoint folder and saves them to the indicated path. For the use of Chemprop within a Python script or a graphical web interface, as well as many options to customize the model, data splits, and performance metrics please consult the instructions on Github 2 or the Chemprop documentation. Hyperparameter optimization can be performed with similar commands, as detailed in the Discussion of Features section.