Continuous Flow Process Optimization with Machine Learning

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

Setup

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 nextorch

Optional Dependencies

You 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-run

Reproduction

You can replicate the research results using the following notebook scripts:

1. notebooks/EDA.ipynb: This notebook performs a feature contribution analysis using AutoSklearn and SHAP tools to gain insights into the dataset.

2. notebooks/Single-Objective Optimization.ipynb: This notebook focuses on single-objective target optimization for continuous flow process optimization, specifically optimizing the yield.

3. notebooks/Multi-Objective Optimization.ipynb: In this notebook, multi-objective optimization is conducted, considering both yield and enantioselectivity as objectives to optimize simultaneously.