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TEDBench/
miae-s-ft

Name: miae-s-ft
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TEDBench — Fine-tuned from pretrained MiAE (structure only)

Variant: miae_s | Parameters: 29M | Layers: 6 | Hidden dim: 512 | Attn heads: 8

This MiAEClassifier was initialized from a pretrained MiAE and fine-tuned on TEDBench for fold classification. It is based on the research presented in the paper Protein Fold Classification at Scale: Benchmarking and Pretraining.

Part of the TEDBench benchmark for protein fold classification (ICML 2026). 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.

Resources

Code: https://github.com/BorgwardtLab/TEDBench
Paper: Protein Fold Classification at Scale: Benchmarking and Pretraining

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

First, install the tedbench library:

pip install tedbench

Then you can load the model using the high-level API:

python

import tedbench

# Loads the fine-tuned MiAE-S fold classifier
model = tedbench.load_model("miae-s-ft")
model.eval()

Alternatively, use the low-level loader:

python

from tedbench.utils.io import load_from_hf

model = load_from_hf("TEDBench/miae-s-ft")
model.eval()

From a Lightning checkpoint

python

from tedbench.model import MiAEClassifier

model = MiAEClassifier.load_from_checkpoint("model.ckpt", weights_only=False)
model.eval()

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},
  year={2026}
}