thomasstruble / aizynthfinder

A tool for retrosynthetic planning

aizynthfinder

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.

Prerequisites

Before you begin, ensure you have met the following requirements:

• Linux, Windows or Mac platforms are supported - as long as the dependencies are supported on these platforms.

The tool has been developed on a Linux platform, but the software has been tested on Windows 10 and macOS Catalina.

• You have installed anaconda or miniconda with python 3.6 or later

Installation

To install aizynthfinder, follow these steps:

• First, install these conda packages

conda install -c rdkit rdkit -y
conda install -c anaconda tensorflow>=2.1.0 -y
conda install graphviz -y

if you have GPU and CUDA libraries enabled on your machine, you can install the tensorflow-gpu package instead.

• Secondly, install the aizynthfinder package

python -m pip install https://github.com/MolecularAI/aizynthfinder/archive/v1.0.0.tar.gz

if you want to install the latest version

or

python -m pip install -e .

if you are a developer, using the repository.

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 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)

Such files can be downloaded from figshare 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.

Testing

Tests uses the pytest package.

To use, first install the dependencies

python -m pip install -r requirements_dev.txt

and then run the tests using

pytest -v

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
• @EBjerrum
• @A-Thakkar

The contributors have limited time for support questions, but pleae 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