| --- |
| 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 |
