A multi-objective reaction optimization framework based on Bayesian optimization
Project description
ReactionOpt
A multi-objective reaction optimization framework based on Bayesian Optimization using Botorch & Ax.
🎯 Overview
ReactionOpt is a Python package designed for optimizing chemical reactions using advanced machine learning techniques. It leverages Bayesian Optimization to efficiently explore reaction spaces and optimize multiple objectives simultaneously (e.g., yield and enantioselectivity).
Key Features
- Multi-objective optimization for reaction conditions (yield & ee optimization)
- Bayesian Optimization powered by Botorch & Ax
- GPU acceleration for large-scale optimization
- Flexible descriptor handling for various reaction parameters
- Automated visualization of optimization results
- High-throughput experimentation support
🚀 Installation
From PyPI (Recommended)
pip install rxnopt
Development Installation
git clone https://github.com/yourusername/reactionopt.git
cd reactionopt
pip install -e .
With Development Dependencies
pip install -e ".[dev]"
📋 Requirements
- Python 3.11+
- PyTorch >= 1.9.0
- Botorch >= 0.6.0
- RDKit >= 2021.9.1
- NumPy, Pandas, Scikit-learn
- Matplotlib, Seaborn (for visualization)
Project details
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