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Changelog

All notable changes to this project will be documented in this file.

The format follows Keep a Changelog and this project adheres to Semantic Versioning.

[0.2.0] — 2026-05-19

Added

  • Type annotations across all tedbench/ Python files (data, layer, model, utils, lr_schedulers). Uses plain torch.Tensor style throughout; py.typed marker enables PEP 561 downstream type-checking.
  • README badges: CI status, PyPI version, Python versions, license.
  • Project logo.

[0.1.0] — 2026-05-13

Added

  • Initial public release accompanying the ICML 2026 paper Protein Fold Classification at Scale: Benchmarking and Pretraining.
  • TEDBench dataset: 462,175 AlphaFold structures across 965 CATH topology classes, with train / val / test splits and an external CATH 4.4 experimental test set (27,638 structures). Available on HuggingFace Hub (TEDBench/ted) and via auto-download from MPCDF.
  • MiAE (Masked Invariant Autoencoders): SE(3)-invariant masked autoencoder for protein backbone frames in three sizes (S / B / L). Pretrained checkpoints, fine-tuned fold classifiers, and from-scratch baselines are all published on HuggingFace Hub.
  • Top-level convenience API: tedbench.load_model(name) and tedbench.list_models().
  • LightningStructureDataset data module supporting both HuggingFace Hub and local auto-downloading backends.
  • Baselines: ESM2, SaProt, ProteinMPNN (scripts in baselines/; requires pip install TEDBench[baselines] for ESM2/SaProt).