This repository contains an easy-to-use Python function for the ESP prediction model from our paper A general model to predict small molecule substrates of enzymes based on machine and deep learning.

Before you can run the ESP prediction function, you need to download and unzip a data folder from Zenodo. Afterwards, this repository should have the following strcuture:

├── code                   
├── data                    
└── README.md

There is a Jupyter notebook "Tutorial ESP prediction.ipynb" in the folder "code" that contains an example on how to use the ESP prediction function.

• python 3.8
• jupyter
• pandas 1.3.1
• torch 1.12.1
• numpy 1.23.1
• rdkit 2022.09.5
• fair-esm 0.4.0
• py-xgboost 1.3.3

The listed packages can be installed using micromamba (or conda or anaconda) and pip as follows:

micromamba create -n esp -c conda-forge pandas==1.3.1 python=3.8 jupyter  numpy==1.23.1 fair-esm==0.4.0 py-xgboost=1.3.3 rdkit=2022.09.5
micromamba activate esp
micromamba remove py-xgboost
pip install xgboost

You can use conda instead of micromamba. This method is tested on Macbook pro 2021 Intel Chip on 14.02.2024.

If you face any issues or problems, please open an issue.