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https://github.com/MolecularAI/aizynthfinder

A tool for retrosynthetic planning
https://github.com/MolecularAI/aizynthfinder

astrazeneca chemical-reactions cheminformatics monte-carlo-tree-search neural-networks reaction-informatics

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A tool for retrosynthetic planning

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README 

        
# AiZynthFinder



[![License](https://img.shields.io/github/license/MolecularAI/aizynthfinder)](https://github.com/MolecularAI/aizynthfinder/blob/master/LICENSE)

[![Tests](https://github.com/MolecularAI/aizynthfinder/workflows/tests/badge.svg)](https://github.com/MolecularAI/aizynthfinder/actions?workflow=tests)

[![codecov](https://codecov.io/gh/MolecularAI/aizynthfinder/branch/master/graph/badge.svg)](https://codecov.io/gh/MolecularAI/aizynthfinder)

[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/python/black)

[![version](https://img.shields.io/github/v/release/MolecularAI/aizynthfinder)](https://github.com/MolecularAI/aizynthfinder/releases)

[![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)



AiZynthFinder 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

supports multiple search algorithms and expansion policies.



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 3.9 - 3.11



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 create "python>=3.9,<3.11" -n aizynth-env



To install, activate the environment and install the package using pypi



    conda activate aizynth-env

    python -m pip install aizynthfinder[all]



for a smaller package, without all the functionality, you can also type



    python -m pip install aizynthfinder



### 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 --all-extras



the `aizynthfinder` package is now installed in editable mode.



## Usage



The tool will install the `aizynthcli` and `aizynthapp` tools

as interfaces to the algorithm:



    aizynthcli --config config_local.yml --smiles smiles.txt

    aizynthapp --config config_local.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 expansion policy network

    3. A trained filter 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 `.

3. Make your changes and commit them: `git commit -m ''`

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)

* [@lakshidaa](https://github.com/Lakshidaa)

* [@helenlai](https://github.com/helenlai)

* [@EBjerrum](https://www.github.com/EBjerrum)

* [@A-Thakkar](https://www.github.com/A-Thakkar)

* [@benteb](https://www.github.com/benteb)



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. ChemRxiv. Preprint. https://doi.org/10.26434/chemrxiv.12465371.v1