---
license: apache-2.0
tags:
  - biology
  - protein-structure-prediction
  - biomolecular-structure-prediction
---

# K-Fold

K-Fold v1.0.0b1 is a beta release under active development.

K-Fold predicts biomolecular complex structures and binding-induced conformational changes.
Through an apo-to-holo diffusion bridge, K-Fold aims to capture conformational changes in systems such as G protein-coupled receptors (GPCRs).

K-Fold supports proteins, DNA, RNA, small molecules, and chemical modifications without multiple sequence alignments (MSAs).

By default, K-Fold prepares protein component structures with [AtlasFold](https://github.com/SeonghwanSeo/atlasfold).
You can also provide apo structures from experiments or other prediction tools.

This repository provides K-Fold model weights. Source code, examples, and documentation are available on [GitHub](https://github.com/SeonghwanSeo/kfold).

## Installation and inference

K-Fold requires Python 3.11 or later.

```bash
pip install kfold
kfold --input query.yaml --out-dir predictions/ --seeds 42
```

Use `--kernel {triton,cuequiv,torch}` to select the backend for K-Fold complex prediction.
Automatic selection prefers Triton, then cuEquivariance, then PyTorch, depending on availability.
Install the `cuequiv` extra (`pip install 'kfold[cuequiv]'`) to enable cuEquivariance kernels.

Model weights and the chemical component dictionary (CCD) are downloaded automatically on first use.

See the [inference guide](https://github.com/SeonghwanSeo/kfold/blob/main/docs/inference.md) for input formats and options, or the [Python API guide](https://github.com/SeonghwanSeo/kfold/blob/main/docs/python_api.md) for use in Python.

## Beta release

K-Fold v1.0.0b1 is a beta release.
Interfaces and prediction results may change before the stable release.

## License

Copyright © 2026 Korea Advanced Institute of Science and Technology (KAIST).

K-Fold is licensed under the [Apache License 2.0](https://github.com/SeonghwanSeo/kfold/blob/main/LICENSE).