README.md

---
pretty_name: Helico Training Data
license: other
license_name: mixed-upstream
language:
  - en
size_categories:
  - 100K<n<1M
tags:
  - biology
  - structural-biology
  - protein-structure-prediction
  - protein-folding
  - alphafold3
  - pdb
  - protein
viewer: false
---

# Helico Training Data

Preprocessed, model-ready training data for
[Helico](https://github.com/Open-Athena/helico) — an AlphaFold3 clone built from
scratch in PyTorch. The core of this repository is **236,326 structures from the
RCSB PDB**, tokenized in AF3 convention, pickled, and published as versioned
snapshots.

> ⚠️ **This is not a `datasets`-loadable dataset.** The payload is Python
> pickles inside split tar archives; `load_dataset()` will not work and the
> dataset viewer is disabled. Unpickling requires the `helico` package to be
> importable, and carries the usual pickle code-execution caveat. See
> [Getting the data](#getting-the-data).

## Contents

| Path | What it is |
|---|---|
| [`processed/pdb-2026-08-08/`](processed/pdb-2026-08-08) | **Current snapshot.** 236,326 structures with contacts, 83.4 GB. [Full documentation →](processed/pdb-2026-08-08/README.md) |
| [`processed/pdb-2026-04-22/`](processed/pdb-2026-04-22) | Superseded, **metadata only** — see the warning below |
| `processed/ccd_cache.pkl` | Parsed Chemical Component Dictionary, 117.8 MB. Shared across snapshots; needed for inference |
| `processed/latest.json` | Names the current snapshot per source type |
| `benchmarks/FoldBench/` | Vendored [FoldBench](https://github.com/BEAM-Labs/FoldBench) evaluation suite, 2.24 GB |

### Snapshots

Data is published as **immutable, date-stamped snapshots** rather than edited in
place. `processed/latest.json` maps a source type to the current snapshot id:

```json
{"schema_version": 1, "sources": {"pdb": "pdb-2026-08-08"}}
```

Each snapshot directory carries a `SOURCE.json` recording the raw data sources,
the preprocessing parameters, and the `git_sha` of the Helico commit that
produced it.

| Snapshot | Structures | Contacts | Status |
|---|---|---|---|
| `pdb-2026-08-08` | 236,326 | ✅ pyconfind `contacts-v1` | **Current** |
| `pdb-2026-04-22` | — | ❌ | ⚠️ Metadata only |

> ⚠️ **`pdb-2026-04-22` contains no structures.** Its `SOURCE.json` claims
> 236,326, but the `structures.tar.*` chunks were never uploaded — a publishing
> bug silently packed an empty directory. Only its manifest, MSA indices, and
> provenance record are present. Use `pdb-2026-08-08`.

## Getting the data

Helico is not published to PyPI; install it from source:

```bash
git clone https://github.com/Open-Athena/helico && cd helico
uv pip install -e ".[dev]"
```

Then let it resolve, fetch and extract everything:

```bash
helico-download                            # follows latest.json
helico-download --snapshot pdb-2026-08-08  # pin explicitly
helico-download --subset ccd-only          # just the CCD cache
```

Files land in `~/.cache/helico/data/` by default; override with `--data-dir` or
the `HELICO_DATA_DIR` env var. Note that `helico-download` deliberately flattens
the snapshot id away — the snapshot is a publishing concern, and the training
code sees a flat `processed/` layout.

To bypass the CLI, download `processed/<snapshot>/structures.tar.*` and
concatenate them in order:

```bash
cat structures.tar.* | tar -xf -
```

## What's in a structure

One pickled `TokenizedStructure` per PDB entry, tokenized in AF3 convention —
one token per protein residue, one per nucleotide, one per **heavy atom** for
ligands. Each carries per-token atom names, elements, coordinates, CCD reference
coordinates, and charges, plus sparse edge lists for covalent bonds and
residue–residue contacts.

The corpus was built by parsing 252,091 mmCIF files from the RCSB archive,
dropping water and hydrogens, and keeping entries at **≤ 9.0 Å resolution** with
at least one polymer chain — **236,326 passed**.

The current snapshot additionally carries side-chain contacts computed by
[pyconfind](https://github.com/timodonnell/pyconfind) with the same
`contacts-v1` parameters that [MarinFold](https://github.com/Open-Athena/MarinFold)
uses, densified at load time into a three-state (contact / no-contact /
unknown) token × token matrix. This supports contact-conditioned, MSA-free
folding.

**[→ Full field-by-field documentation, provenance, and generation pipeline](processed/pdb-2026-08-08/README.md)**

## Train/val split

The split is applied at load time from `release_date` in the manifest — it is
not baked into the files. Helico's defaults match AF3, Protenix v1, and
OpenFold3-preview2 so metrics are directly comparable:

| Split | Rule | Count |
|---|---|---|
| Train | `release_date < 2021-09-30` | 170,926 |
| Val | `2022-05-01 ≤ release_date ≤ 2023-01-12` | 9,716 |

Structures in the 2021-09-30 → 2022-05-01 gap are in neither split — AF3's
deliberate leakage-prevention design.

> ⚠️ **A temporal split is not a redundancy split.** 38.2% of validation
> structures share at least one chain sequence *verbatim* with training, and
> 18.4% share every chain sequence. Any holdout built from these files by date
> needs an explicit sequence-identity filter before it will support a
> generalization claim. Details in the
> [snapshot README](processed/pdb-2026-08-08/README.md#-a-temporal-split-is-not-a-redundancy-split).

Note also that no cluster-based weighted sampling, deduplication, or
molecule-type rebalancing is applied — the corpus is published as-is.

## MSAs

Precomputed alignments are **not hosted here** — the two `*_msa_index.pkl` files
in each snapshot are byte-offset maps enabling O(1) random reads into archives
you obtain separately:

- `rcsb_raw_msa.tar` (131 GB) — `https://boltz1.s3.us-east-2.amazonaws.com/rcsb_raw_msa.tar`
- `openfold_raw_msa.tar` (88 GB) — `https://boltz1.s3.us-east-2.amazonaws.com/openfold_raw_msa.tar`

Lookup is content-addressed by sequence hash, not PDB ID. The OpenFold index is
published for completeness but is not usable through Helico's current lookup
path (its members are keyed by UniProt accession, and that mapping is not
implemented).

## Benchmarks

`benchmarks/FoldBench/` is a vendored checkout of the
[FoldBench](https://github.com/BEAM-Labs/FoldBench) evaluation suite (cloned
2026-03-04, ground-truth CIFs from 2025-05-20), including precomputed MSAs for
its targets and vendored copies of baseline algorithm code. It is bundled for
reproducible benchmarking; FoldBench is MIT licensed and carries its own
`LICENSE`, as does each vendored algorithm. See the FoldBench paper in
[Nature Communications](https://doi.org/10.1038/s41467-025-67127-3).

## Provenance and terms

This repository aggregates data from several upstream sources under differing
terms, which is why no single license is declared:

| Component | Origin |
|---|---|
| Structures | [RCSB PDB](https://www.rcsb.org/) — archive distributed without copyright restriction (CC0 1.0) |
| Chemical Component Dictionary | [wwPDB](https://www.wwpdb.org/) |
| MSA archives (indexed, not hosted) | Published by [Boltz](https://github.com/jwohlwend/boltz); OpenFold alignments derive from [OpenFold](https://github.com/aqlaboratory/openfold) |
| Contacts | [pyconfind](https://github.com/timodonnell/pyconfind) |
| `benchmarks/FoldBench/` | [FoldBench](https://github.com/BEAM-Labs/FoldBench), MIT; includes vendored third-party algorithm code under its own licenses |

If you use this data, please cite the underlying PDB entries and the upstream
projects above. Usage is governed by the terms of those sources.

## Related

- **Code and training recipes:** [`Open-Athena/helico`](https://github.com/Open-Athena/helico)
- **Model weights:** [`timodonnell/helico`](https://huggingface.co/timodonnell/helico)