metadata
library_name: tedbench
license: bsd-3-clause
pipeline_tag: graph-ml
tags:
- protein
- structure
- fold-classification
- tedbench
TEDBench — Supervised from scratch (structure only)
Variant: miae_l | Parameters: 339M | Layers: 24 | Hidden dim: 1024 | Attn heads: 16
This MiAEClassifier was trained from scratch on TEDBench without pretraining. It is part of the research presented in the paper Protein Fold Classification at Scale: Benchmarking and Pretraining.
MiAE is an SE(3)-invariant masked autoencoder that masks up to 90% of backbone frames and reconstructs the full structure with a lightweight decoder. It establishes a strong recipe for protein fold classification on the TEDBench benchmark.
Architecture sizes
| Variant | Params | Layers | Hidden dim | Attn heads |
|---|---|---|---|---|
miae_s |
29 M | 6 | 512 | 8 |
miae_b |
102 M | 12 | 768 | 12 |
miae_l |
339 M | 24 | 1 024 | 16 |
Append +model.use_seq_input=true to miae_b for the +seq variant.
Usage
Load from the HuggingFace Hub
from tedbench.utils.io import load_from_hf
model = load_from_hf("TEDBench/miae-l-sc")
model.eval()
From a Lightning checkpoint
from tedbench.model import MiAEClassifier
model = MiAEClassifier.load_from_checkpoint("model.ckpt", weights_only=False)
model.eval()
Links
- Code: BorgwardtLab/TEDBench
- Paper: arXiv:2605.18552
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},
year={2026}
}