README.md

metadata

library_name: tedbench
license: bsd-3-clause
pipeline_tag: graph-ml
tags:
  - protein
  - structure
  - fold-classification
  - tedbench

TEDBench — Pretrained autoencoder (structure + sequence)

Variant: miae_b + seq  |  Parameters: 102M  |  Layers: 12  |  Hidden dim: 768  |  Attn heads: 12

This repository contains the pretrained MiAE checkpoint introduced in the paper Protein Fold Classification at Scale: Benchmarking and Pretraining. Use it as a feature extractor or as the starting point for fine-tuning.

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

+seq variant — sequence embeddings are concatenated to the geometric encoder input (model.use_seq_input=true).

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.

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

from tedbench.utils.io import load_from_hf

model = load_from_hf("TEDBench/miae-b-seq")
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 and Manolache, Andrei and Niepert, Mathias and Borgwardt, Karsten},
  booktitle={Proceedings of the 43rd International Conference on Machine Learning},
  year={2026}
}