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aizynthfinder-3.6.0
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مشاهده بیشترتوضیحات
Retrosynthetic route finding using neural network guided Monte-Carlo tree search
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | aizynthfinder-3.6.0 |
| نام | aizynthfinder |
| نسخه کتابخانه | 3.6.0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Molecular AI group |
| ایمیل نویسنده | samuel.genheden@astrazeneca.com |
| آدرس صفحه اصلی | https://github.com/MolecularAI/aizynthfinder/ |
| آدرس اینترنتی | https://pypi.org/project/aizynthfinder/ |
| مجوز | MIT |
# 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 3.8 - 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 create "python>=3.8,<3.10" -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 -E all
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.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)
* [@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. 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)
نیازمندی
| مقدار | نام |
|---|---|
| >=7.5.1,<8.0.0 | ipywidgets |
| >=3.0.0,<4.0.0 | jinja2 |
| >=1.0.0,<2.0.0 | jupyter |
| >=1.3.3,<2.0.0 | jupytext |
| >=2.4,<3.0 | networkx |
| >=8.2.0,<9.0.0 | more-itertools |
| >=1.2.10,<2.0.0 | deprecated |
| >=1.0.0,<2.0.0 | pandas |
| >=9.0.0,<10.0.0 | pillow |
| >=2.23.0,<3.0.0 | requests |
| >=1.0.0,<2.0.0 | rdchiral |
| >=2022.3.3,<2023.0.0 | rdkit |
| >=3.6.1,<4.0.0 | tables |
| >=2.8.0,<3.0.0 | tensorflow |
| >=4.42.1,<5.0.0 | tqdm |
| >=1.24.0,<2.0.0) | grpcio |
| >=2.1.0,<3.0.0) | tensorflow-serving-api |
| >=3.10.1,<4.0.0) | pymongo |
| >=1.1.0,<2.0.0) | route-distances |
| >=1.0,<2.0) | scipy |
| >=3.0.0,<4.0.0) | matplotlib |
| >=0.5.0,<0.6.0) | timeout-decorator |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.8,<3.10 | Python |
نحوه نصب
نصب پکیج whl aizynthfinder-3.6.0:
pip install aizynthfinder-3.6.0.whl
نصب پکیج tar.gz aizynthfinder-3.6.0:
pip install aizynthfinder-3.6.0.tar.gz