synrbl 0.0.8

Synthesis Rebalancing Framework for Computational Chemistry

SynRBL: Synthesis Rebalancing Framework

SynRBL is a toolkit tailored for computational chemistry, aimed at correcting imbalances in chemical reactions. It employs a dual strategy: a rule-based method for adjusting non-carbon elements and an mcs-based (maximum common substructure) technique for carbon element adjustments.

Table of Contents

• Installation
• Usage
• Contributing
• License
• Acknowledgments

Installation

To install and set up the SynRBL framework, follow these steps. Please ensure you have Python 3.11 or later installed on your system.

Prerequisites

• Python 3.11
• RDKit == 2023.9.4
• joblib==1.3.2
• seaborn==0.13.2
• xgoost==2.0.3
• scikit_learn==1.4.1.post1
• imbalanced_learn==0.12.0
• reportlab==4.1.0

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 synrbl-env
source synrbl-env/bin/activate  # On Windows use `synrbl-env\Scripts\activate`

Or Conda

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

3. Install with pip: Clone the SynRBL repository from GitHub and install it:

pip install synrbl

4. Verify Installation: After installation, you can verify that SynRBL is correctly installed by running a simple test or checking the package version.

python -c "import synrbl; print(synrbl.__version__)"

Usage

1. Jupyter Notebook:

from synrbl import Balancer

2. Command line

python -m synrbl run --help

3. Reproduce the experiment

python -m synrbl run -o validation_results.csv -p 4 ./Data/Validation_set/validation_set.csv

Contributing

• Tieu-Long Phan
• Klaus Weinbauer

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)