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TEDBench/
miae-l-ft

Name: miae-l-ft
Rating: 2.5 (6 reviews)

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TEDBench — Fine-tuned from pretrained MiAE (structure only)

Variant: miae_l | Parameters: 339M | Layers: 24 | Hidden dim: 1024 | Attn heads: 16

This MiAEClassifier was initialised from a pretrained MiAE and fine-tuned on TEDBench for fold classification.

Part of the TEDBench benchmark for protein fold classification (ICML 2026), introduced in the paper Protein Fold Classification at Scale: Benchmarking and Pretraining.

Code: GitHub Repository
Paper: arXiv:2605.18552

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.

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-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}
}