{"id":13422951,"url":"https://github.com/MolecularAI/aizynthfinder","last_synced_at":"2025-03-15T13:30:23.328Z","repository":{"id":37192667,"uuid":"271546468","full_name":"MolecularAI/aizynthfinder","owner":"MolecularAI","description":"A tool for retrosynthetic planning","archived":false,"fork":false,"pushed_at":"2024-04-30T15:56:34.000Z","size":3910,"stargazers_count":536,"open_issues_count":9,"forks_count":124,"subscribers_count":29,"default_branch":"master","last_synced_at":"2024-05-06T00:03:26.209Z","etag":null,"topics":["astrazeneca","chemical-reactions","cheminformatics","monte-carlo-tree-search","neural-networks","reaction-informatics"],"latest_commit_sha":null,"homepage":"https://molecularai.github.io/aizynthfinder/","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/MolecularAI.png","metadata":{"files":{"readme":"README.md","changelog":"CHANGELOG.md","contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":"CITATION.cff","codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2020-06-11T12:57:07.000Z","updated_at":"2024-06-03T15:20:02.867Z","dependencies_parsed_at":"2023-02-15T10:15:51.533Z","dependency_job_id":"d679bc95-ecdd-457b-a6f6-1e8c8642046a","html_url":"https://github.com/MolecularAI/aizynthfinder","commit_stats":{"total_commits":72,"total_committers":7,"mean_commits":"10.285714285714286","dds":0.5,"last_synced_commit":"9e44989213c11f1bb647a00b8756e0c76a8f4b52"},"previous_names":[],"tags_count":16,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MolecularAI%2Faizynthfinder","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MolecularAI%2Faizynthfinder/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MolecularAI%2Faizynthfinder/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/MolecularAI%2Faizynthfinder/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/MolecularAI","download_url":"https://codeload.github.com/MolecularAI/aizynthfinder/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243735736,"owners_count":20339518,"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":["astrazeneca","chemical-reactions","cheminformatics","monte-carlo-tree-search","neural-networks","reaction-informatics"],"created_at":"2024-07-30T23:01:00.506Z","updated_at":"2025-03-15T13:30:22.867Z","avatar_url":"https://github.com/MolecularAI.png","language":"Python","funding_links":[],"categories":["General Chemistry","Molecule Retrosynthesis Pathways","🔬 Domain-Specific Applications","Ranked by starred repositories"],"sub_categories":["⚛ Chemistry \u0026 Materials"],"readme":"# AiZynthFinder\n\n[![License](https://img.shields.io/github/license/MolecularAI/aizynthfinder)](https://github.com/MolecularAI/aizynthfinder/blob/master/LICENSE)\n[![Tests](https://github.com/MolecularAI/aizynthfinder/workflows/tests/badge.svg)](https://github.com/MolecularAI/aizynthfinder/actions?workflow=tests)\n[![codecov](https://codecov.io/gh/MolecularAI/aizynthfinder/branch/master/graph/badge.svg)](https://codecov.io/gh/MolecularAI/aizynthfinder)\n[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/python/black)\n[![version](https://img.shields.io/github/v/release/MolecularAI/aizynthfinder)](https://github.com/MolecularAI/aizynthfinder/releases)\n[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/MolecularAI/aizynthfinder/blob/master/contrib/notebook.ipynb)\n\nAiZynthFinder is a tool for retrosynthetic planning. The default algorithm is based on a Monte Carlo tree search that recursively breaks down a molecule to purchasable precursors. The tree search is guided by a policy that suggests possible precursors by utilizing a neural network trained on a library of known reaction templates. This setup is completely customizable as the tool\nsupports multiple search algorithms and expansion policies.\n\nAn introduction video can be found here: [https://youtu.be/r9Dsxm-mcgA](https://youtu.be/r9Dsxm-mcgA)\n\n## Prerequisites\n\nBefore you begin, ensure you have met the following requirements:\n\n* Linux, Windows or macOS platforms are supported - as long as the dependencies are supported on these platforms.\n\n* You have installed [anaconda](https://www.anaconda.com/) or [miniconda](https://docs.conda.io/en/latest/miniconda.html) with python 3.9 - 3.11\n\nThe tool has been developed on a Linux platform, but the software has been tested on Windows 10 and macOS Catalina.\n\n## Installation\n\n### For end-users\n\nFirst time, execute the following command in a console or an Anaconda prompt\n\n    conda create \"python\u003e=3.9,\u003c3.11\" -n aizynth-env\n\nTo install, activate the environment and install the package using pypi\n\n    conda activate aizynth-env\n    python -m pip install aizynthfinder[all]\n\nfor a smaller package, without all the functionality, you can also type\n\n    python -m pip install aizynthfinder\n\n### For developers\n\nFirst clone the repository using Git.\n\nThen execute the following commands in the root of the repository\n\n    conda env create -f env-dev.yml\n    conda activate aizynth-dev\n    poetry install --all-extras\n\nthe `aizynthfinder` package is now installed in editable mode.\n\n\n## Usage\n\nThe tool will install the `aizynthcli` and `aizynthapp` tools\nas interfaces to the algorithm:\n\n    aizynthcli --config config_local.yml --smiles smiles.txt\n    aizynthapp --config config_local.yml\n\n\nConsult the documentation [here](https://molecularai.github.io/aizynthfinder/) for more information.\n\nTo use the tool you need\n\n    1. A stock file\n    2. A trained expansion policy network\n    3. A trained filter policy network (optional)\n\nSuch files can be downloaded from [figshare](https://figshare.com/articles/AiZynthFinder_a_fast_robust_and_flexible_open-source_software_for_retrosynthetic_planning/12334577) and [here](https://figshare.com/articles/dataset/A_quick_policy_to_filter_reactions_based_on_feasibility_in_AI-guided_retrosynthetic_planning/13280507) or they can be downloaded automatically using\n\n```\ndownload_public_data my_folder\n```\n\nwhere ``my_folder`` is the folder that you want download to.\nThis will create a ``config.yml`` file that you can use with either ``aizynthcli`` or ``aizynthapp``.\n\n## Development\n\n### Testing\n\nTests uses the ``pytest`` package, and is installed by `poetry`\n\nRun the tests using:\n\n    pytest -v\n\nThe full command run on the CI server is available through an `invoke` command\n\n    invoke full-tests\n\n ### Documentation generation\n\nThe documentation is generated by Sphinx from hand-written tutorials and docstrings\n\nThe HTML documentation can be generated by\n\n    invoke build-docs\n\n## Contributing\n\nWe welcome contributions, in the form of issues or pull requests.\n\nIf you have a question or want to report a bug, please submit an issue.\n\n\nTo contribute with code to the project, follow these steps:\n\n1. Fork this repository.\n2. Create a branch: `git checkout -b \u003cbranch_name\u003e`.\n3. Make your changes and commit them: `git commit -m '\u003ccommit_message\u003e'`\n4. Push to the remote branch: `git push`\n5. Create the pull request.\n\nPlease use ``black`` package for formatting, and follow ``pep8`` style guide.\n\n\n## Contributors\n\n* [@SGenheden](https://www.github.com/SGenheden)\n* [@lakshidaa](https://github.com/Lakshidaa)\n* [@helenlai](https://github.com/helenlai)\n* [@EBjerrum](https://www.github.com/EBjerrum)\n* [@A-Thakkar](https://www.github.com/A-Thakkar)\n* [@benteb](https://www.github.com/benteb)\n\nThe contributors have limited time for support questions, but please do not hesitate to submit an issue (see above).\n\n## License\n\nThe software is licensed under the MIT license (see LICENSE file), and is free and provided as-is.\n\n## References\n\n1. Thakkar A, Kogej T, Reymond J-L, et al (2019) Datasets and their influence on the development of computer assisted synthesis planning tools in the pharmaceutical domain. Chem Sci. https://doi.org/10.1039/C9SC04944D\n2. Genheden S, Thakkar A, Chadimova V, et al (2020) AiZynthFinder: a fast, robust and flexible open-source software for retrosynthetic planning. ChemRxiv. Preprint. https://doi.org/10.26434/chemrxiv.12465371.v1","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FMolecularAI%2Faizynthfinder","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FMolecularAI%2Faizynthfinder","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FMolecularAI%2Faizynthfinder/lists"}