A chemical reaction can be defined as a transformation of one set of chemical compounds to another. The correct mapping of the rearrangement of chemical compound atoms during the transformation is essential for capturing the essence of a chemical reaction. This task, widely known as atom-to-atom mapping, has proven quite challenging. Nevertheless, novel approaches are being published frequently. The main goal of this repository is to curate existing open-source chemical reaction compound atom-to-atom mapping libraries.

A minimal virtual environment can be created using Conda as follows:

conda env create -f environment.yaml

conda activate atom-to-atom-mapping

The package can be locally installed using pip as follows:

pip install --no-build-isolation -e .

The following chemical reaction compound atom-to-atom mapping libraries are currently supported:

1. The Chytorch RxnMap ^[1] library utilizes a Transformer model for processing chemical compound graphs.
2. The EPAM Indigo ^[2] library utilizes a chemical compound graph-matching algorithm.
3. The LocalMapper ^[3] library utilizes a human-in-the-loop Message Passing Neural Network model.
4. The RXNMapper ^[4] library utilizes a chemically agnostic attention-guided Transformer model.

The following updates are currently planned for version v.2024.07:

• Create the /documentation directory.
• Create the /notebooks directory.
• Create the /scripts directory.

The contents of this repository are published under the MIT license. Please refer to individual references for more details regarding the license information of external resources utilized within this repository.

If you are interested in contributing to this repository by reporting bugs, suggesting improvements, or submitting feedback, feel free to use GitHub Issues.

[1] Nugmanov, R., Dyubankova, N., Gedich, A., and Wegner, J.K. Bidirectional Graphormer for Reactivity Understanding: Neural Network Trained to Reaction Atom-to-atom Mapping Task. J. Chem. Inf. Model., 2022, 62, 14, 3307–3315.

[2] EPAM Indigo: https://lifescience.opensource.epam.com/indigo/index.html. Accessed on: June 1st, 2024.

[3] Chen, S., An, S., Babazade. R., and Jung, Y. Precise Atom-to-atom Mapping for Organic Reactions via Human-in-the-loop Machine Learning. Nat. Commun., 15, 2250, 2024.

[4] Schwaller, P., Hoover, B., Reymond, J., Strobelt, H., and Laino, T. Extraction of Organic Chemistry Grammar from Unsupervised Learning of Chemical Reactions. Sci. Adv., 7, 15, eabe4166, 2021.