逆合成规划的工具_python_代码_下载_retrosynthetic planning

# AiZynthFinder [](https://github.com/MolecularAI/aizynthfinder/blob/master/LICENSE) [](https://github.com/MolecularAI/aizynthfinder/actions?workflow=tests) [](https://codecov.io/gh/MolecularAI/aizynthfinder) [](https://github.com/python/black) [](https://github.com/MolecularAI/aizynthfinder/releases) [](https://colab.research.google.com/github/MolecularAI/aizynthfinder/blob/master/contrib/notebook.ipynb) AiZynthFinder is a tool for retrosynthetic planning. The 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. An introduction video can be found here: [https://youtu.be/r9Dsxm-mcgA](https://youtu.be/r9Dsxm-mcgA) ## Prerequisites Before you begin, ensure you have met the following requirements: * Linux, Windows or macOS platforms are supported - as long as the dependencies are supported on these platforms. * You have installed [anaconda](https://www.anaconda.com/) or [miniconda](https://docs.conda.io/en/latest/miniconda.html) with python 3.6 - 3.9 The tool has been developed on a Linux platform, but the software has been tested on Windows 10 and macOS Catalina. ## Installation ### For end-users First time, execute the following command in a console or an Anaconda prompt conda env create -f https://raw.githubusercontent.com/MolecularAI/aizynthfinder/master/env-users.yml And if you want to update the environment conda env update -n aizynth-env -f https://raw.githubusercontent.com/MolecularAI/aizynthfinder/master/env-users.yml The package is now installed in a new conda environment, that you need to activate each time you want to use it conda activate aizynth-env ### For developers First clone the repository using Git. Then execute the following commands in the root of the repository conda env create -f env-dev.yml conda activate aizynth-dev poetry install the `aizynthfinder` package is now installed in editable mode. ### Troubleshooting If the above simple instructions does not work, here are the more detailed instructions. You might have to modify conda channels or similar if the dependencies fails to install on your OS. First, install these conda packages conda install -c conda-forge "rdkit=>2019.09.1" -y conda install graphviz -y Secondly, install the ``aizynthfinder`` package python -m pip install https://github.com/MolecularAI/aizynthfinder/archive/v3.4.0.tar.gz if you want to install the latest version or, if you have cloned this repository conda install poetry python poetry > Note on the graphviz installation: this package does not depend on any third-party python interfaces to graphviz but instead calls the `dot` executable directly. If the executable is not in the `$PATH` environmental variable, the generation of route images will not work. If unable to install it properly with the default conda channel, try using `-c anaconda`. ## Usage The tool will install the ``aizynthcli`` and ``aizynthapp`` tools as interfaces to the algorithm: ``` aizynthcli --config config.yml --smiles smiles.txt aizynthapp --config config.yml ``` Consult the documentation [here](https://molecularai.github.io/aizynthfinder/) for more information. To use the tool you need 1. A stock file 2. A trained rollout policy network (including the Keras model and the list of unique templates) 3. A trained filer policy network (optional) Such 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 ``` download_public_data my_folder ``` where ``my_folder`` is the folder that you want download to. This will create a ``config.yml`` file that you can use with either ``aizynthcli`` or ``aizynthapp``. ## Development ### Testing Tests uses the ``pytest`` package, and is installed by `poetry` Run the tests using: pytest -v The full command run on the CI server is available through an `invoke` command invoke full-tests ### Documentation generation The documentation is generated by Sphinx from hand-written tutorials and docstrings The HTML documentation can be generated by invoke build-docs ## Contributing We welcome contributions, in the form of issues or pull requests. If you have a question or want to report a bug, please submit an issue. To contribute with code to the project, follow these steps: 1. Fork this repository. 2. Create a branch: `git checkout -b <branch_name>`. 3. Make your changes and commit them: `git commit -m '<commit_message>'` 4. Push to the remote branch: `git push` 5. Create the pull request. Please use ``black`` package for formatting, and follow ``pep8`` style guide. ## Contributors * [@SGenheden](https://www.github.com/SGenheden) * [@EBjerrum](https://www.github.com/EBjerrum) * [@A-Thakkar](https://www.github.com/A-Thakkar) The contributors have limited time for support questions, but please do not hesitate to submit an issue (see above). ## License The software is licensed under the MIT license (see LICENSE file), and is free and provided as-is. ## References 1. 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 2. Genheden S, Thakkar A, Chadimova V, et al (2020) AiZynthFinder: a fast, robust and flexible open-source software for retrosynthetic planning. J. Cheminf. https://jcheminf.biomedcentral.com/articles/10.1186/s13321-020-00472-1 3. Genheden S, Engkvist O, Bjerrum E (2020) A Quick Policy to Filter Reactions Based on Feasibility in AI-Guided Retrosynthetic Planning. ChemRxiv. Preprint. https://doi.org/10.26434/chemrxiv.13280495.v1 4. Genheden S, Engkvist O, Bjerrum E (2021) Clustering of synthetic routes using tree edit distance. J. Chem. Inf. Model. 61:3899–3907 [https://doi.org/10.1021/acs.jcim.1c00232](https://doi.org/10.1021/acs.jcim.1c00232) 5. Genheden S, Engkvist O, Bjerrum E (2022) Fast prediction of distances between synthetic routes with deep learning. Mach. Learn. Sci. Technol. 3:015018 [https://doi.org/10.1088/2632-2153/ac4a91](https://doi.org/10.1088/2632-2153/ac4a91)