metadata
library_name: tedbench
license: bsd-3-clause
pipeline_tag: graph-ml
tags:
- protein
- structure
- fold-classification
- tedbench
TEDBench — Pretrained autoencoder (structure only)
Variant: miae_b | Parameters: 102M | Layers: 12 | Hidden dim: 768 | Attn heads: 12
This repository contains the pretrained MiAE-B (base) checkpoint, a self-supervised model for protein structure representation learning introduced in the paper Protein Fold Classification at Scale: Benchmarking and Pretraining.
MiAE (Masked Invariant Autoencoders) is an $\mathrm{SE(3)}$-invariant autoencoder that uses an extremely high masking ratio (up to 90%) of backbone frames to reconstruct full protein structures. It can be used as a feature extractor or as the starting point for fine-tuning on protein fold classification tasks.
- Authors: Dexiong Chen, Andrei Manolache, Mathias Niepert, Karsten Borgwardt
- Official Code: 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
Using the tedbench library:
from tedbench.utils.io import load_from_hf
model = load_from_hf("TEDBench/miae-b")
model.eval()
From a Lightning checkpoint
from tedbench.model import MiAE
model = MiAE.load_from_checkpoint("model.ckpt", weights_only=False)
model.eval()
Citation
@inproceedings{chen2026tedbench,
title={Protein Fold Classification at Scale: Benchmarking and Pretraining},
author={Chen, Dexiong explorer and Manolache, Andrei and Niepert, Mathias and Borgwardt, Karsten},
booktitle={Proceedings of the 43rd International Conference on Machine Learning (ICML)},
year={2026}
}