Datasets:
Tasks:
Graph Machine Learning
Sub-tasks:
multi-class-classification
Languages:
English
Size:
100K - 1M
ArXiv:
License:
File size: 3,294 Bytes
d3cd349 eb9c18a 576252c eb9c18a 576252c eb9c18a 576252c eb9c18a 576252c eb9c18a d3cd349 576252c eb9c18a 576252c eb9c18a 576252c eb9c18a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 | ---
language:
- en
license: bsd-3-clause
size_categories:
- 100K<n<1M
task_categories:
- graph-ml
task_ids:
- multi-class-classification
pretty_name: TEDBench
tags:
- protein
- structure
- fold-classification
- tedbench
- alphafold
- cath
---
# TEDBench
[**Paper**](https://huggingface.co/papers/2605.18552) | [**GitHub**](https://github.com/BorgwardtLab/TEDBench)
Large-scale, non-redundant benchmark for **protein fold classification** built from
[Encyclopedia of Domains (TED)](https://zenodo.org/records/13908086) annotations
projected onto the Foldseek-clustered AlphaFold Database.
This dataset was presented in the paper [Protein Fold Classification at Scale: Benchmarking and Pretraining](https://huggingface.co/papers/2605.18552).
## Dataset statistics
| Split | Structures |
|-------|----------:|
| Train | 369,740 |
| Val | 46,217 |
| Test | 46,218 |
**965 CATH topology (T-level) classes** — rare topologies with fewer
than 10 samples are merged into architecture-level "x" classes.
## 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 |
| `label` | `ClassLabel` | CATH topology class index (names are CATH T-level code strings, e.g. `"3.40.50.300"`) |
`label` is a `datasets.ClassLabel` whose `.names` list contains the CATH
topology strings (e.g. `"3.40.50.300"`), so the dataset is fully self-contained.
## Usage
### Load from HuggingFace
```python
from datasets import load_dataset
import torch
ted = load_dataset("TEDBench/ted")
sample = ted["train"][0]
coords = torch.tensor(sample["coords"]) # [L, 3, 3]
plddt = torch.tensor(sample["plddt"]) # [L]
label = sample["label"] # int index
# Decode label → CATH code string:
cath_code = ted["train"].features["label"].int2str(label)
```
### Use in TEDBench training scripts
```bash
# Fine-tune pretrained MiAE-B on TEDBench
python main_finetune_ted.py datamodule=hf_ted \
pretrained_model_path=TEDBench/miae-b \
experiment=finetune_ted_base_n1g8
# Linear probing with pretrained MiAE-B
python main_linprobe_ted.py datamodule=hf_ted \
pretrained_model_path=TEDBench/miae-b
# Evaluate a fine-tuned model on the CATH 4.4 external test set
python main_test_ted.py datamodule=hf_cath_test \
pretrained_model_path=TEDBench/miae-b-ft
```
## Source data
- AlphaFold DB clustered by
[Foldseek](https://afdb-cluster.steineggerlab.workers.dev) (pLDDT > 80)
- CATH topology annotations from
[TED v365M](https://zenodo.org/records/13908086)
## 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 |