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Message Passing Neural Networks for Molecule Property Prediction

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Chemprop

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Chemprop is a repository containing message passing neural networks for molecular property prediction.

Documentation can be found here.

There are tutorial notebooks in the examples/ directory.

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.

License: Chemprop is free to use under the MIT License. The Chemprop logo is free to use under CC0 1.0.

References: Please cite the appropriate papers if Chemprop is helpful to your research.

Selected Applications: Chemprop has been successfully used in the following works.

Version 1.x

For users who have not yet made the switch to Chemprop v2.0, please reference the following resources.

v1 Documentation

  • Documentation of Chemprop v1 is available here. Note that the content of this site is several versions behind the final v1 release (v1.7.1) and does not cover the full scope of features available in chemprop v1.
  • The v1 README is the best source for documentation on more recently-added features.
  • Please also see descriptions of all the possible command line arguments in the v1 args.py file.

v1 Tutorials and Examples

  • Benchmark scripts - scripts from our 2023 paper, providing examples of many features using Chemprop v1.6.1
  • ACS Fall 2023 Workshop - presentation, interactive demo, exercises on Google Colab with solution key
  • Google Colab notebook - several examples, intended to be run in Google Colab rather than as a Jupyter notebook on your local machine
  • nanoHUB tool - a notebook of examples similar to the Colab notebook above, doesn't require any installation
  • These slides provide a Chemprop tutorial and highlight additions as of April 28th, 2020

v1 Known Issues

We have discontinued support for v1 since v2 has been released, but we still appreciate v1 bug reports and will tag them as v1-wontfix so the community can find them easily.

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