ByteDance-Seed/felis

ByteDance-Seed/felis

Description: FELIS: Free Energy of Ligand-protein InteractionS

Language: Python

License: Apache-2.0

Stars: 5

Forks: 1

Open issues: 1

Created: 2026-05-06T08:54:43Z

Pushed: 2026-05-28T06:08:02Z

Default branch: main

Fork: no

Archived: no

README:

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Felis

Felis (Free Energy of Ligand-protein InteractionS) is an open-source toolkit for automated and scalable protein-ligand absolute binding free energy (ABFE) calculations. It is designed for high-throughput structure-based drug discovery and supports a practical ABFE workflow without the scaffold constraints of RBFE methods.

Getting started

Prerequisites

• Python version >= 3.11
• CUDA >= 12.6

Dependencies

OpenMM & OpenMMTools

You can install OpenMM and OpenMMTools using conda:

conda install -c conda-forge openmm cuda-version=12.6
conda config --add channels omnia --add channels conda-forge
conda install openmmtools
conda remove jax jaxlib

Gromacs

You can easily install Gromacs using the apt package manager on Debian/Ubuntu-based systems:

sudo apt update
sudo apt install gromacs

Once the installation is complete, verify that your Gromacs version is 2022.5 or higher:

gmx --version

ProLIF

You can install ProLIF from source with the provided patch:

git clone https://github.com/chemosim-lab/ProLIF.git
cd ProLIF
git checkout v2.0.3
git apply ../submodule/prolif.patch
pip install .

Installation

After resolving the dependencies above, you can install Felis and its required Python packages by running:

pip install .

Quick Start Example

We provide an example ABFE calculation in the examples/abfe/ directory. This example requires 8 GPUs to run.

cd examples/abfe/
bash run.sh

The script prepares input files from pl_bfe_dataset and runs the full ABFE workflow.

License

• The code portion of this project is licensed under the Apache License, Version 2.0.
• The dataset in the pl_bfe_dataset/ directory is licensed under the Creative Commons Attribution 4.0 International License.

Citation

If you find Felis useful for your research and applications, feel free to give us a star ⭐ or cite us using:

@misc{liu2026developmentlargescalebenchmarksproteinligand,
title={Development and large-scale benchmarks of a protein-ligand absolute binding free energy toolkit},
author={Yu Liu and Ailun Wang and Yu Xia and Zhi Wang and Wen Yan},
year={2026},
eprint={2603.22274},
archivePrefix={arXiv},
primaryClass={physics.comp-ph},
url={https://arxiv.org/abs/2603.22274},
}

About ByteDance Seed Team

Founded in 2023, ByteDance Seed Team is dedicated to crafting the industry's most advanced AI foundation models. The team aspires to become a world-class research team and make significant contributions to the advancement of science and society.