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 Tutorial, and Jupyter Notebook Tutorials pages.

If you use Chemprop to train or develop a model in your own work, we would appreciate if you cite the following papers:

[1]

Kevin Yang, Kyle Swanson, Wengong Jin, Connor Coley, Philipp Eiden, Hua Gao, Angel Guzman-Perez, Timothy Hopper, Brian Kelley, Miriam Mathea, Andrew Palmer, Volker Settels, Tommi Jaakkola, Klavs Jensen, and Regina Barzilay. Analyzing learned molecular representations for property prediction. Journal of Chemical Information and Modeling, 59(8):3370–3388, 2019. PMID: 31361484. URL: https://doi.org/10.1021/acs.jcim.9b00237, arXiv:https://doi.org/10.1021/acs.jcim.9b00237, doi:10.1021/acs.jcim.9b00237.

[2]

Esther Heid, Kevin P. Greenman, Yunsie Chung, Shih-Cheng Li, David E. Graff, Florence H. Vermeire, Haoyang Wu, William H. Green, and Charles J. McGill. Chemprop: a machine learning package for chemical property prediction. Journal of Chemical Information and Modeling, 64(1):9–17, 2024. PMID: 38147829. URL: https://doi.org/10.1021/acs.jcim.3c01250, arXiv:https://doi.org/10.1021/acs.jcim.3c01250, doi:10.1021/acs.jcim.3c01250.

Contents:

• Quickstart
• Installation
• Command Line Tutorial
• Jupyter Notebook Tutorials
• CLI Reference
• API Reference

Indices and tables

• Index

• Module Index

• Search Page