Skip to main content

Molecular Property Prediction with Message Passing Neural Networks

Project description

ChemProp Logo

Chemprop

PyPI - Python Version PyPI version Anaconda-Server Badge Build Status Documentation Status License: MIT Downloads Downloads Downloads

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.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

chemprop-2.2.0.tar.gz (128.2 kB view details)

Uploaded Source

Built Distribution

chemprop-2.2.0-py3-none-any.whl (137.2 kB view details)

Uploaded Python 3

File details

Details for the file chemprop-2.2.0.tar.gz.

File metadata

  • Download URL: chemprop-2.2.0.tar.gz
  • Upload date:
  • Size: 128.2 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.12.9

File hashes

Hashes for chemprop-2.2.0.tar.gz
Algorithm Hash digest
SHA256 f076b27feb2f9f5d7b1079a2a61e56fc468d7ba6bf5bd3fadafdb6cb585b9bff
MD5 2e424d411aa8a443d90e7b4ee51cee1c
BLAKE2b-256 a1a29718489536a5f5a0461eaa8024fe7d363a855c00ec55ffa4c6315f178c2e

See more details on using hashes here.

File details

Details for the file chemprop-2.2.0-py3-none-any.whl.

File metadata

  • Download URL: chemprop-2.2.0-py3-none-any.whl
  • Upload date:
  • Size: 137.2 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.1.0 CPython/3.12.9

File hashes

Hashes for chemprop-2.2.0-py3-none-any.whl
Algorithm Hash digest
SHA256 20c8ee7db482ca5f0dfeaf1b41f8c222be12cfd8c17a02e01172d76eab18a9c2
MD5 c05ed8ecd26d6351116adf6e43e9f90e
BLAKE2b-256 0f2798c3549a2d73e4c5e9380205722964e425aa465e84ac0e630e491f5426fb

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page