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.

Code: GitHub Repository
Paper: Hugging Face Paper Page

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

python

from tedbench.utils.io import load_from_hf

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

From a Lightning checkpoint

python

from tedbench.model import MiAE

model = MiAE.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}
}