---
license: cc-by-nc-4.0
tags:
  - molecular-dynamics
  - biomolecular-trajectories
  - diffusion
  - protein
  - protein-ligand
  - alphafold3
library_name: protenix
---

# BioKinema: Physically Grounded Generative Modeling of All-Atom Biomolecular Dynamics

[![Paper](https://img.shields.io/badge/Paper-bioRxiv-green)](https://www.biorxiv.org/content/10.64898/2026.02.15.705956v1)
[![Code License](https://img.shields.io/badge/Code%20License-Apache_2.0-green?style=flat-square)](https://github.com/IDEA-XL/BioKinema/blob/main/LICENSE)
[![Data License](https://img.shields.io/badge/Data%20License-CC%20By%20NC%204.0-red?style=flat-square)](https://github.com/IDEA-XL/BioKinema/blob/main/DATA_LICENSE)
[![GitHub Link](https://img.shields.io/badge/GitHub-blue?style=flat-square&logo=github)](https://github.com/IDEA-XL/BioKinema)

## Introduction

**BioKinema** is a physically grounded generative model that predicts continuous-time, all-atom biomolecular trajectories at a fraction of the cost of traditional molecular dynamics (MD) simulations. It is built on top of [Protenix](https://github.com/bytedance/Protenix) (ByteDance's AlphaFold 3 reproduction) and extends it with a temporal-attention mechanism derived from Langevin dynamics, so a single model can roll out MD-like trajectories at arbitrary, possibly non-uniform frame intervals.

The temporal-attention bias follows a stretched-exponential decay `B_ij = -λ |t_i - t_j|^β`, where `λ` is a per-head learnable decay (ALiBi-initialised) and `β` is a fixed time-scaling exponent selected per model variant.

> This HuggingFace repository hosts the released **weights** and processed **data**. For installation, inference, training, the data pipeline, and the manuscript benchmark code, see the [BioKinema GitHub repository](https://github.com/IDEA-XL/BioKinema).

## Repository Contents

| File | Description | Size |
|------|-------------|------|
| `BioKinema_atlas+misato+mdposit_sqrt.pt` | **`sqrt`** checkpoint (EMA). For protein–ligand complexes and **short-time** MD. Trained on Atlas + MISATO + MDposit with `β = 0.5`. | ~3.9 GB |
| `BioKinema_CATH+octapeptide_beta0.25.pt` | **`beta=0.25`** checkpoint (EMA). For **long-time, single-chain** protein MD. Trained on MSR (CATH / MegaSim / octapeptides) with `β = 0.25`; adds a TICA-dynamics loss. | ~3.9 GB |
| `biokinema_codec_bundle.tar` | Processed MISATO / MDposit / unbinding data in a lossless compressed codec (one template bioassembly per trajectory + a stacked-coordinate array). Used by `sqrt` training. | ~41 GB |

### Which Checkpoint to Use

- **Complexes / protein–ligand, or short MD** → `BioKinema_atlas+misato+mdposit_sqrt.pt` (run with `β = 0.5`).
- **Long single-chain protein MD / kinetics** → `BioKinema_CATH+octapeptide_beta0.25.pt` (run with `β = 0.25`).

The exponent `β` **must match the checkpoint** at inference time (pass it via `--beta`).

## Usage

Clone the [BioKinema repository](https://github.com/IDEA-XL/BioKinema), install the environment, then run inference:

```bash
bash inference.sh \
    --checkpoint_path ./checkpoints/BioKinema_atlas+misato+mdposit_sqrt.pt \
    --dump_dir ./output \
    --input_file ./experiments/atlas_benchmark/init_frames/7lp1_A_R1_0.cif \
    --beta 0.5
```

DDP checkpoints (with a `module.` prefix) are handled automatically by the inference runner.

### Codec Bundle

```bash
tar -xf biokinema_codec_bundle.tar -C $BIOKINEMA_UNBINDING_ROOT
# -> $BIOKINEMA_UNBINDING_ROOT/{misato_codec,mdposit_codec,unbinding_codec}
```

Point each dataset's `bioassembly_dict_dir` at the corresponding `*_codec/` directory; the data loader auto-detects and decompresses on the fly.

## Training Data

- **Atlas** — public ATLAS MD database (preprocessing scripts shipped in the code repository).
- **MSR (CATH / Octapeptides / MegaSim)** — Zenodo: `10.5281/zenodo.15629740`, `10.5281/zenodo.15641199`, `10.5281/zenodo.15641184`.
- **MISATO / MDposit / unbinding** — released here as the compressed codec bundle above.

## Citation

```bibtex
@article{feng2026physically,
  title={Physically Grounded Generative Modeling of All-Atom Biomolecular Dynamics},
  author={Feng, Bin and Zhang, Jiying and Zhang, Xinni and Zhang, Ming and Barth, Patrick and Liu, Zijing and Li, Yu},
  journal={bioRxiv},
  pages={2026--02},
  year={2026},
  publisher={Cold Spring Harbor Laboratory}
}
```

## Acknowledgements

This project was built based on [Protenix](https://github.com/bytedance/Protenix), an open-source biomolecular structure prediction framework developed by ByteDance.

## Contact

For questions or collaborations, please open an issue or contact us at [fengbin@idea.edu.cn](mailto:fengbin@idea.edu.cn).