Datasets:

TEDBench
/

ted

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 | GitHub

Large-scale, non-redundant benchmark for protein fold classification built from Encyclopedia of Domains (TED) annotations projected onto the Foldseek-clustered AlphaFold Database.

This dataset was presented in the paper Protein Fold Classification at Scale: Benchmarking and Pretraining.

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

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

# 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 (pLDDT > 80)
CATH topology annotations from TED v365M

Citation

@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