SynCat

SynCat is a novel graph-based framework for classifying chemical reactions by leveraging molecule-level cross-attention for precise reagent detection and role assignment. To overcome the limitations of existing methods, it ensures permutation invariance through a pairwise summation of participant embeddings, which balances mechanistic specificity with an order-independent representation. This approach has demonstrated superior performance over established fingerprints like DRFP and RXNFP, achieving a mean classification accuracy of 0.988 and enhanced scalability on benchmark datasets.

Step-by-Step Installation Guide

1. Python Installation: Ensure that Python 3.11 or later is installed on your system. You can download it from python.org.

2. Creating a Virtual Environment (Optional but Recommended): It's recommended to use a virtual environment to avoid conflicts with other projects or system-wide packages. Use the following commands to create and activate a virtual environment:

python -m venv syncat-env
source syncat-env/bin/activate  

Or Conda

conda create --name syncat-env python=3.11
conda activate syncat-env

3. Cloning and Installing SynCat: Clone the SynCat repository from GitHub and install it:

git clone https://github.com/phuocchung123/SynCat.git
cd SynCat
pip install -r requirements.txt
pip install black flake8 pytest # black for formating, flake8 for checking format, pytest for testing

Setting Up Your Development Environment

Before you start, ensure your local development environment is set up correctly. Pull the latest version of the main branch to start with the most recent stable code.

git checkout main
git pull

Working on New Features

1. Create a New Branch:
   For every new feature or bug fix, create a new branch from the main branch. Name your branch meaningfully, related to the feature or fix you are working on.

   git checkout -b feature/your-feature-name

2. Develop and Commit Changes:
   Make your changes locally, commit them to your branch. Keep your commits small and focused; each should represent a logical unit of work.

   git commit -m "Describe the change"

3. Run Quality Checks:
   Before finalizing your feature, run the following commands to ensure your code meets our formatting standards and passes all tests:

   ./lint.sh # Check code format
   pytest Test # Run tests

   Fix any issues or errors highlighted by these checks.

Integrating Changes

1. Rebase onto Staging:
   Once your feature is complete and tests pass, rebase your changes onto the staging branch to prepare for integration.

   git fetch origin
   git rebase origin/staging

   Carefully resolve any conflicts that arise during the rebase.

2. Push to Your Feature Branch: After successfully rebasing, push your branch to the remote repository.

   git push origin feature/your-feature-name

3. Create a Pull Request: Open a pull request from your feature branch to the staging branch. Ensure the pull request description clearly describes the changes and any additional context necessary for review.

Contributing

• Phuoc-Chung Nguyen Van
• Tieu-Long Phan

Publication

SynCat: A light weight graph neural network model to classify chemical reactions

License

This project is licensed under MIT License - see the License file for details.

Acknowledgments

This project has received funding from the European Unions Horizon Europe Doctoral Network programme under the Marie-Skłodowska-Curie grant agreement No 101072930 (TACsy -- Training Alliance for Computational)