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https://github.com/MolecularAI/aizynthtrain

Tools to train synthesis prediction models
https://github.com/MolecularAI/aizynthtrain

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Tools to train synthesis prediction models

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# aizynthtrain



aizynthtrain is a collection of routines, configurations and pipelines for training synthesis prediction models for the AiZynthFinder software.



## 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 - 3.10



The tool has been developed on a Linux platform.



## Installation



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 aizynthtrain

    poetry install

    

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



Now, you need to create a Jupyter Kernel for this environment



    conda activate aizynthtrain

    ipython kernel install --name "aizynthtrain" --user



## Usage



There is a number of example configurations and SLURM scripts in the `configs/uspto` folder that

was used to retrain the USPTO-based expansion model for AiZynthFinder.



The example configurations can be used as-is to reproduce the modelling, whereas the SLURM scripts

migt have to be adjusted to your system.



To use the given configurations, follow this procedure:



For the training and validation of the expansion models, you need a number of data files that can be downloaded from Zenodo. 

In addition the pipelines create a lof of artifcats and therefore, you will start with setting up a folder for your modelling



1. Create a new folder where the pipelines will be executed using



    python configs/uspto/setup_folder.py PATH_TO_YOUR_FOLDER



2. cd PATH_TO_YOUR_FOLDER



3. Perform the pipelines from the `rxnutils` package to download and prepare the USPTO datasets for modelling: https://molecularai.github.io/reaction_utils/uspto.html



The `rxnutils` package pipelines should have produced a file `uspto_data_mapped.csv` that will be used as the starting point of the template-extraction pipeline



4. Adapt and execute the `configs/uspto/template_pipeline.sh` SLURM script on your machine.



The template-extraction pipeline will produce a number of artifacts, the most important being

- `reaction_selection_report.html` that is the report of the reaction selection

- `template_selection_report.html` that is the report of the template selection

- `uspto_template_library.csv` and `uspto_ringbreaker_template_library.csv` that are the final reactions and reaction templates that will be used to train the expansion model.



5. Adapt and execute the `configs/uspto/expansion_pipeline.sh` SLURM script on your machine.



The expansion model pipeline will produce these important artifacts



- `uspto_expansion_model_report.html` that is the report of the model training and validation

- `uspto_keras_model.hdf5` the trained Keras model

- `uspto_unique_templates.csv.gz` the template library for AiZynthFinder



You can also execute the `configs/uspto/ringbreaker_pipeline.sh` to train a RingBreaker model.



### Testing



Tests uses the ``pytest`` package, and is installed by `poetry`



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 `.

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)

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



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 Appache 2.0 license (see LICENSE file), and is free and provided as-is.



## References



1. Genheden S, Norrby PO, Engkvist O (2022) AiZynthTrain: robust, reproducible, and extensible pipelines for training synthesis prediction models. ChemRxiv. Prerint. https://doi.org/10.26434/chemrxiv-2022-kls5q

2. Kannas C, Thakkar A, Bjerrum E, Genheden S (2022) rxnutils – A Cheminformatics Python Library for Manipulating Chemical Reaction Data. ChemRxiv. https://doi.org/10.26434/chemrxiv-2022-wt440-v2

3. 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

4. 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