synrbl 0.0.5

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

• Repository Structure
• Installation
• Usage
• Contributing
• License
• Acknowledgments

Repository Structure

SynRBL is organized into several key components, each dedicated to a specific aspect of chemical data processing:

Main Components

• SynRBL/: Main package directory
  • SynProcessor/: Data processing module
  • SynRuleImputer/: Rule-based imputation module
  • SynMCSImputer/: MCS-based imputation module
  • SynChemImputer/: MCS-based imputation module
  • SynVis/: Data visualization module

Test Suite

• tests/: Test scripts and related files
  • SynProcessor/: Tests for SynExtract module
  • SynRuleImputer/: Tests for SynRuleImpute module
  • SynMCSImputer/: Tests for MCS-based imputation module
  • SynChemImputer/: Tests for MCS-based imputation module
  • SynVis/: Tests for SynVis module

Additional Resources

• License: License document
• README.md: Overview and documentation
• setup.py: Installation
• .gitignore: Configuration for ignoring certain files and directories

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

git clone https://github.com/TieuLongPhan/SynRBL.git
cd SynRBL
pip install .

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

from synrbl import Balancer

TODO

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)