This repository contains the code implementation for the paper titled "Continuous Flow Process Optimization Aided by Machine Learning for a Pharmaceutical Intermediate."
Here is the MOO results for our paper to balance the yields and the enantioselectivity for continuous flow chemistry
To set up the required environment, follow these steps:
# Create and activate a virtual environment
mamba create -n flow python=3.7
mamba activate flow
# Install PyTorch and dependencies
mamba install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia
# Install RDKit and Mordred
mamba install -c rdkit -c mordred-descriptor mordred rdkit
# Install other dependencies
mamba install cudatoolkit=11.7 py-xgboost-gpu shap
mamba install jupyter ipykernel rich fire pandas
# Install NexTorch
python -m pip install nextorch
# Clone and test the NexTorch repo
git clone https://github.com/VlachosGroup/nextorch.git
cd nextorch
pytest --pyargs nextorchYou can also install optional dependencies for additional functionality:
# Openbox (for HTML Visualization)
python -m pip install "openbox[extra]"
# EDBO
cd software
git clone https://github.com/b-shields/edbo
cd edbo
python -m pip install -e .
# AutoSklearn
sudo apt-get install build-essential swig python3-dev
mamba create -n autosklearn python=3.8
mamba activate autosklearn
git clone https://github.com/automl/auto-sklearn.git
cd auto-sklearn
git submodule update --init --recursive
pip install -e ".[test,docs,examples]" --dry-runYou can replicate the research results using the following notebook scripts:
-
notebooks/EDA.ipynb: This notebook performs a feature contribution analysis using AutoSklearn and SHAP tools to gain insights into the dataset. -
notebooks/Single-Objective Optimization.ipynb: This notebook focuses on single-objective target optimization for continuous flow process optimization, specifically optimizing the yield. -
notebooks/Multi-Objective Optimization.ipynb: In this notebook, multi-objective optimization is conducted, considering both yield and enantioselectivity as objectives to optimize simultaneously.
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