---
language:
- en
license: bsd-3-clause
size_categories:
- 100K<n<10M
task_categories:
- graph-ml
pretty_name: TEDBench-AFDB (pretraining)
tags:
- protein
- structure
- pretraining
- tedbench
- alphafold
- foldseek
---

# TEDBench-AFDB (pretraining corpus)

[**Paper**](https://huggingface.co/papers/2605.18552) | [**GitHub**](https://github.com/BorgwardtLab/TEDBench)

Representative proteins from Foldseek-clustered AlphaFold Database (pLDDT > 80), used to pretrain **MiAE (Masked Invariant Autoencoders)** in the **TEDBench** benchmark. 

TEDBench is a large-scale, non-redundant benchmark for protein fold classification constructed from the Encyclopedia of Domains (TED) and Foldseek-clustered AlphaFold structures.

## Dataset statistics

| Split | Structures |
|-------|----------:|
| Train | 742,183 |
| Val   | 7,496 |
| **Total** | **749,679** |

One representative structure per Foldseek sequence-similarity cluster.

## Schema

| Column | Type | Description |
|--------|------|-------------|
| `name` | `string` | AlphaFold domain identifier (e.g. `AF-Q8IYB3-F1`) |
| `sequence` | `string` | Amino-acid sequence (single-letter code) |
| `coords` | `[L, 3, 3]` float32 | Backbone N/Cα/C coordinates (Å) |
| `plddt` | `[L]` float32 | Per-residue AlphaFold pLDDT confidence score |
| `residue_index` | `[L]` int64 | Residue index in the original AlphaFold model |
| `seq_ids` | `[L]` int64 | ESM-tokenised sequence IDs |

No `label` column — this dataset is for **unsupervised pretraining** only.

## Usage

### Load from HuggingFace

```python
from datasets import load_dataset
import torch

afdb = load_dataset("TEDBench/afdb", split="train")
sample = afdb[0]

coords = torch.tensor(sample["coords"])   # [L, 3, 3]
plddt  = torch.tensor(sample["plddt"])    # [L]
```

### Pretrain MiAE using this dataset

```bash
python main_pretrain.py datamodule=hf_afdbfs

# Multi-GPU (effective batch size 4096)
python main_pretrain.py \
    experiment=tedbench_base_n4g8 \
    datamodule=hf_afdbfs
```

## Source data

- [Foldseek](https://afdb-cluster.steineggerlab.workers.dev) cluster representatives from AlphaFold Database v4 (pLDDT > 80)

## Citation

```bibtex
@inproceedings{chen2026tedbench,
  title={Protein Fold Classification at Scale: Benchmarking and Pretraining},
  author={Chen, Dexiong and Manolache, Andrei and Niepert, Mathias and Borgwardt, Karsten},
  booktitle={Proceedings of the 43rd International Conference on Machine Learning (ICML)},
  year={2026}
}
```

## License

BSD-3-Clause