{"id":13768673,"url":"https://github.com/chemprop/chemprop","last_synced_at":"2025-05-13T15:12:43.581Z","repository":{"id":37085323,"uuid":"167628014","full_name":"chemprop/chemprop","owner":"chemprop","description":"Message Passing Neural Networks for Molecule Property Prediction","archived":false,"fork":false,"pushed_at":"2025-05-01T17:59:10.000Z","size":840543,"stargazers_count":1949,"open_issues_count":68,"forks_count":628,"subscribers_count":35,"default_branch":"main","last_synced_at":"2025-05-01T18:43:14.060Z","etag":null,"topics":["chemistry","drug-discovery","machine-learning","neural-networks"],"latest_commit_sha":null,"homepage":"https://chemprop.csail.mit.edu","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/chemprop.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE.txt","code_of_conduct":null,"threat_model":null,"audit":null,"citation":"CITATIONS.bib","codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2019-01-25T23:33:22.000Z","updated_at":"2025-04-29T20:11:13.000Z","dependencies_parsed_at":"2023-10-20T18:47:38.098Z","dependency_job_id":"dcf27406-8e01-4237-b9c4-e0e44eae9e91","html_url":"https://github.com/chemprop/chemprop","commit_stats":{"total_commits":1299,"total_committers":73,"mean_commits":"17.794520547945204","dds":0.4996150885296382,"last_synced_commit":"54bd7b376960ed34f5b678dc00f5fc3f13e87f50"},"previous_names":[],"tags_count":29,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/chemprop%2Fchemprop","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/chemprop%2Fchemprop/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/chemprop%2Fchemprop/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/chemprop%2Fchemprop/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/chemprop","download_url":"https://codeload.github.com/chemprop/chemprop/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":253969266,"owners_count":21992264,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["chemistry","drug-discovery","machine-learning","neural-networks"],"created_at":"2024-08-03T16:01:24.350Z","updated_at":"2025-05-13T15:12:38.567Z","avatar_url":"https://github.com/chemprop.png","language":"Python","funding_links":[],"categories":["Libraries","Frameworks, Libraries, and Software Tools","📚 فهرست","Machine Learning","🔬 Domain-Specific Applications","Biomedical Research \u0026 Drug Discovery"],"sub_categories":["Machine Learning","MS/MS prediction","کتابخانه هاي داروسازي","🧬 Biology \u0026 Medicine"],"readme":"\u003cpicture\u003e\n  \u003csource media=\"(prefers-color-scheme: dark)\" srcset=\"docs/source/_static/images/logo/chemprop_logo_dark_mode.svg\"\u003e\n  \u003cimg alt=\"ChemProp Logo\" src=\"docs/source/_static/images/logo/chemprop_logo.svg\"\u003e\n\u003c/picture\u003e\n\n# Chemprop\n\n[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/chemprop)](https://badge.fury.io/py/chemprop)\n[![PyPI version](https://badge.fury.io/py/chemprop.svg)](https://badge.fury.io/py/chemprop)\n[![Anaconda-Server Badge](https://anaconda.org/conda-forge/chemprop/badges/version.svg)](https://anaconda.org/conda-forge/chemprop)\n[![Build Status](https://github.com/chemprop/chemprop/workflows/tests/badge.svg)](https://github.com/chemprop/chemprop/actions/workflows/tests.yml)\n[![Documentation Status](https://readthedocs.org/projects/chemprop/badge/?version=main)](https://chemprop.readthedocs.io/en/main/?badge=main)\n[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\n[![Downloads](https://static.pepy.tech/badge/chemprop)](https://pepy.tech/project/chemprop)\n[![Downloads](https://static.pepy.tech/badge/chemprop/month)](https://pepy.tech/project/chemprop)\n[![Downloads](https://static.pepy.tech/badge/chemprop/week)](https://pepy.tech/project/chemprop)\n\nChemprop is a repository containing message passing neural networks for molecular property prediction.\n\nDocumentation can be found [here](https://chemprop.readthedocs.io/en/main/).\n\nThere are tutorial notebooks in the [`examples/`](https://github.com/chemprop/chemprop/tree/main/examples) directory.\n\nChemprop 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](https://docs.google.com/spreadsheets/u/3/d/e/2PACX-1vRshySIknVBBsTs5P18jL4WeqisxDAnDE5VRnzxqYEhYrMe4GLS17w5KeKPw9sged6TmmPZ4eEZSTIy/pubhtml). 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.\n\n**License:** Chemprop is free to use under the [MIT License](LICENSE.txt). The Chemprop logo is free to use under [CC0 1.0](docs/source/_static/images/logo/LICENSE.txt).\n\n**References**: Please cite the appropriate papers if Chemprop is helpful to your research.\n\n- Chemprop was initially described in the papers [Analyzing Learned Molecular Representations for Property Prediction](https://pubs.acs.org/doi/abs/10.1021/acs.jcim.9b00237) for molecules and [Machine Learning of Reaction Properties via Learned Representations of the Condensed Graph of Reaction](https://doi.org/10.1021/acs.jcim.1c00975) for reactions.\n- The interpretation functionality (available in v1, but not yet implemented in v2) is based on the paper [Multi-Objective Molecule Generation using Interpretable Substructures](https://arxiv.org/abs/2002.03244).\n- Chemprop now has its own dedicated manuscript that describes and benchmarks it in more detail: [Chemprop: A Machine Learning Package for Chemical Property Prediction](https://doi.org/10.1021/acs.jcim.3c01250).\n- A paper describing and benchmarking the changes in v2.0.0 is forthcoming.\n\n**Selected Applications**: Chemprop has been successfully used in the following works.\n\n- [A Deep Learning Approach to Antibiotic Discovery](https://www.cell.com/cell/fulltext/S0092-8674(20)30102-1) - _Cell_ (2020): Chemprop was used to predict antibiotic activity against _E. coli_, leading to the discovery of [Halicin](https://en.wikipedia.org/wiki/Halicin), a novel antibiotic candidate. Model checkpoints are availabile on [Zenodo](https://doi.org/10.5281/zenodo.6527882).\n- [Discovery of a structural class of antibiotics with explainable deep learning](https://www.nature.com/articles/s41586-023-06887-8) - _Nature_ (2023): Identified a structural class of antibiotics selective against methicillin-resistant _S. aureus_ (MRSA) and vancomycin-resistant enterococci using ensembles of Chemprop models, and explained results using Chemprop's interpret method.\n- [ADMET-AI: A machine learning ADMET platform for evaluation of large-scale chemical libraries](https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btae416/7698030?utm_source=authortollfreelink\u0026utm_campaign=bioinformatics\u0026utm_medium=email\u0026guestAccessKey=f4fca1d2-49ec-4b10-b476-5aea3bf37045): Chemprop was trained on 41 absorption, distribution, metabolism, excretion, and toxicity (ADMET) datasets from the [Therapeutics Data Commons](https://tdcommons.ai). The Chemprop models in ADMET-AI are available both as a web server at [admet.ai.greenstonebio.com](https://admet.ai.greenstonebio.com) and as a Python package at [github.com/swansonk14/admet_ai](https://github.com/swansonk14/admet_ai).\n- A more extensive list of successful Chemprop applications is given in our [2023 paper](https://doi.org/10.1021/acs.jcim.3c01250)\n\n## Version 1.x\n\nFor users who have not yet made the switch to Chemprop v2.0, please reference the following resources.\n\n### v1 Documentation\n\n- Documentation of Chemprop v1 is available [here](https://chemprop.readthedocs.io/en/v1.7.1/). 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.\n- The v1 [README](https://github.com/chemprop/chemprop/blob/v1.7.1/README.md) is the best source for documentation on more recently-added features.\n- Please also see descriptions of all the possible command line arguments in the v1 [`args.py`](https://github.com/chemprop/chemprop/blob/v1.7.1/chemprop/args.py) file.\n\n### v1 Tutorials and Examples\n\n- [Benchmark scripts](https://github.com/chemprop/chemprop_benchmark) - scripts from our 2023 paper, providing examples of many features using Chemprop v1.6.1\n- [ACS Fall 2023 Workshop](https://github.com/chemprop/chemprop-workshop-acs-fall2023) - presentation, interactive demo, exercises on Google Colab with solution key\n- [Google Colab notebook](https://colab.research.google.com/github/chemprop/chemprop/blob/v1.7.1/colab_demo.ipynb) - several examples, intended to be run in Google Colab rather than as a Jupyter notebook on your local machine\n- [nanoHUB tool](https://nanohub.org/resources/chempropdemo/) - a notebook of examples similar to the Colab notebook above, doesn't require any installation\n  - [YouTube video](https://www.youtube.com/watch?v=TeOl5E8Wo2M) - lecture accompanying nanoHUB tool\n- These [slides](https://docs.google.com/presentation/d/14pbd9LTXzfPSJHyXYkfLxnK8Q80LhVnjImg8a3WqCRM/edit?usp=sharing) provide a Chemprop tutorial and highlight additions as of April 28th, 2020\n\n### v1 Known Issues\n\nWe have discontinued support for v1 since v2 has been released, but we still appreciate v1 bug reports and will tag them as [`v1-wontfix`](https://github.com/chemprop/chemprop/issues?q=label%3Av1-wontfix+) so the community can find them easily.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fchemprop%2Fchemprop","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fchemprop%2Fchemprop","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fchemprop%2Fchemprop/lists"}