README.md

DORA-XGB: An improved enzymatic reaction feasibility classifier trained using a novel synthetic data approach

Chainani, Yash, Zhuofu Ni, Kevin M. Shebek, Linda J. Broadbelt, and Keith EJ Tyo. "DORA-XGB: an improved enzymatic reaction feasibility classifier trained using a novel synthetic data approach." Molecular Systems Design & Engineering (2025)

This public repository holds the supervised learning reaction feasibility models, DORA_XGB. For examples on how to use our models, see scripts/run_example.py or notebooks/DORA_XGB_examples.ipynb.

Environment setup & installation options:

1. Clone this repository:

To use our DORA-XGB models, clone this repository then create a new python environment or use an existing one. Subsequently, from the same directory as the setup.py file, pip install DORA-XGB:

conda create -n DORA_XGB_env python=3.8
pip install -e.

2. Install from the PyPI repository:

The most convenient way to begin using our DORA-XGB models may be to directly install them from the python package index (PyPI):

pip install DORA-XGB

Running DORA-XGB with docker:

We have also created a docker container for users to deploy our models within a containerized environment. To begin, run the following in the same directory as the dockerfile to build a docker image with the name dora_xgb:

docker build -t dora_xgb .

After building the docker image locally, spin up a container with an interactive bash shell:

docker run -ti dora_xgb /bin/bash

In this interactive bash shell, the run_example script and be run simply using:

python run_example.py

To edit the contents of each script, you can download the vim text editor in the docker container:

apt-get update && apt-get install -y vim

To shut down a docker container and return to your terminal, simply type exit into the interactive bash shell.