Welcome to Chemprop’s documentation!#
This website contains documentation for Chemprop, a PyTorch-based framework for training and evaluating message-passing neural networks (MPNNs) for molecular property prediction. The package was originally developed for Yang et al.[1] and further described in Heid et al.[2].
To get started with Chemprop, check out the Quickstart page, and for more detailed information, see the Installation, Command Line Tutorials, and Jupyter Notebook Examples pages.
Note
Chemprop recently underwent a ground-up rewrite and new major release (v2.0.0). A helpful transition guide from Chemprop v1 to v2 can be found here. This includes a side-by-side comparison of CLI argument options, a list of which arguments will be implemented in later versions of v2, and a list of changes to default hyperparameters.
If you use Chemprop to train or develop a model in your own work, we would appreciate if you cite the following papers: