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TEDBench/
miae-s

Name: miae-s
Rating: 2.5 (9 reviews)

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TEDBench — Pretrained autoencoder (structure only)

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

This repository contains a pretrained MiAE checkpoint. Use it as a feature extractor or as the starting point for fine-tuning on protein structure tasks.

MiAE (Masked Invariant Autoencoder) is an $\mathrm{SE(3)}$-invariant masked autoencoder that masks up to 90% of backbone frames and reconstructs the full structure with a lightweight decoder. It was introduced in the paper Protein Fold Classification at Scale: Benchmarking and Pretraining.

Paper: Protein Fold Classification at Scale: Benchmarking and Pretraining
Repository: BorgwardtLab/TEDBench

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

You can load the model using the tedbench library:

python

from tedbench.utils.io import load_from_hf

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

Alternatively, using the library's high-level API:

python

import tedbench

model = tedbench.load_model("miae-s")
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 (ICML)},
  year={2026}
}