README.md

---
pretty_name: DuoPose Processed Database
language:
  - en
license: other
tags:
  - protein-ligand
  - molecular-docking
  - pose-ranking
  - decoydb
  - pytorch
size_categories:
  - 10K<n<100K
---

# DuoPose Processed Database

This repository contains the processed target-level database used by the
DuoPose project and its evaluated model variants, including GeoPose and the
DuoPose family.
The archive is reconstructed from the DecoyDB-derived structural source. It is
**not a copy of the complete upstream DecoyDB repository**. The processing
pipeline used the project code to convert receptor, native-ligand, and decoy
PDBQT structures into one PyTorch/PyTorch Geometric object per target.

## Repository files

```text
processed_full.tar
processed_full.tar.sha256
README.md
```

- `processed_full.tar`: archive containing the processed target-level files.
- `processed_full.tar.sha256`: SHA-256 checksum for the archive.
- `README.md`: dataset description, reconstruction details, download and
  verification instructions.

The archive contains one top-level directory, `processed_full/`, and exactly
61,085 target-level `.pt` files.

## Dataset statistics

| Item | Value |
|---|---:|
| Complete raw target directories identified | 61,104 |
| Successfully processed targets | 61,085 |
| Raw targets excluded from formal statistics | 19 |
| Excluded fraction | 0.031095% |
| Processed coverage of complete raw targets | 99.968905% |
| Extracted processed tensor bytes | 5,675,197,832 |
| Train targets | 48,868 |
| Validation targets | 6,108 |
| Test targets | 6,109 |
| Total fixed-split targets | 61,085 |

The 19 excluded raw targets did not produce valid processed objects. Because
the excluded fraction is very small, no imputation was performed. Only
successfully reconstructed targets were included in the formal processed
dataset and the fixed train/validation/test split lists.

The split lists are maintained in the companion DuoPose source-code
repository. The data archive itself does not create separate `train/`, `val/`,
or `test/` directories; all target files are stored together under
`processed_full/`.

## Upstream source and reconstruction

The original structural source is DecoyDB:

- [DecoyDB GitHub repository](https://github.com/spatialdatasciencegroup/DecoyDB)
- [DecoyDB Hugging Face dataset card](https://huggingface.co/datasets/jiangteam/DecoyDB)

This project reconstructed a target-level pose-ranking database rather than
redistributing the complete upstream repository. The main processing stages
were:

1. Scan the DecoyDB-derived structural archive and group files by target.
2. Parse receptor, native-ligand, and decoy coordinates from PDBQT files.
3. Compute the geometric center of the native ligand.
4. Extract receptor atoms within an 8 Å pocket radius.
5. Construct 32-dimensional protein atom features.
6. Parse decoy `MODEL` blocks and retain decoys with atom counts compatible
   with the native ligand.
7. Recompute decoy RMSD values using Kabsch alignment.
8. Save one target-level `.pt` dictionary for each successfully processed
   target.
9. Re-read native ligands to add 32-dimensional ligand atom features and
   ligand element labels.

The corresponding source-code entry points are:

```text
scripts/process_full_dataset.py
scripts/augment_ligand_features.py
dataset.py::PocketSubgraphExtractor
dataset.py::DTIDataset
scripts/verify_processed_dataset.py
```

The upstream auxiliary CSV RMSD column is not used as the label source for the
processed `.pt` objects. RMSD values are recomputed from the coordinates and
are used for decoy selection, curriculum/ranking weights, and reporting; RMSD
is not provided directly as a model node feature.

## Processed file structure

Each file is a target-level dictionary serialized with PyTorch. The expected
schema is:

```text
<target_id>.pt

protein_graph: torch_geometric.data.Data
  x: [N_protein, 32]       # protein atom features
  pos: [N_protein, 3]      # coordinates in Å

true_ligand_coords: [N_ligand, 3]       # native pose coordinates in Å
ligand_atom_features: [N_ligand, 32]    # ligand atom features
ligand_elements: list[str]

decoys: list[dict]
  coords: [N_ligand, 3]    # decoy coordinates in Å
  rmsd: float              # Kabsch-aligned RMSD

target_id: str
```

The cached target files contain the protein graph node features and positions,
but the final model radius graph is constructed dynamically during model
execution. The model pipeline uses a dynamic graph cutoff of approximately
12 Å.

## Task semantics

Each target contains:

- one receptor pocket;
- one native ligand pose, treated as the positive pose;
- multiple decoy ligand poses, treated as negative candidates;
- recomputed RMSD values for pose quality and ranking-related procedures.

The dataset is used for native-versus-decoy pose ranking rather than as a
pre-expanded binary-classification table.

## Download

The processed DuoPose dataset can be downloaded from Hugging Face with:

```bash
hf download Kilig33/DuoPose-database processed_full.tar \
  --repo-type dataset \
  --local-dir .
```

Download the archive checksum with:

```bash
hf download Kilig33/DuoPose-database processed_full.tar.sha256 \
  --repo-type dataset \
  --local-dir .
```

The repository ID is `Kilig33/DuoPose-database`.

## Archive integrity verification

The archive checksum currently recorded for `processed_full.tar` is:

```text
70d69cb4b8aa74653d0863d43c16ae73389ebee71b7145760e00b17d2b656387  processed_full.tar
```

### Windows PowerShell

```powershell
$expected = ((Get-Content .\processed_full.tar.sha256).Trim() -split '\s+')[0].ToLower()
$actual = (Get-FileHash -Algorithm SHA256 .\processed_full.tar).Hash.ToLower()

if ($expected -ne $actual) {
    throw "SHA-256 verification failed"
}
Write-Output "SHA-256 verification passed"
```

### Linux or macOS

```bash
sha256sum -c processed_full.tar.sha256
```

The expected result is:

```text
processed_full.tar: OK
```

## Extraction

### Windows, Linux, or macOS

```bash
tar -xf processed_full.tar
```

After extraction, the expected directory is:

```text
processed_full/
|-- <target_id>.pt
|-- <target_id>.pt
`-- ...
```

The archive contains exactly 61,085 `.pt` files under `processed_full/`.

## Expected project location

After extraction, place or link the directory into the companion source-code
repository as:

```text
DuoPose/data/processed_full/
```

For example:

```text
DuoPose/
|-- data/
|   `-- processed_full/
|       |-- <target_id>.pt
|       `-- ...
|-- dataset.py
|-- scripts/
`-- ...
```

The code repository provides the authoritative train/validation/test lists
and the per-file SHA-256 manifest. After restoring the archive, the processed
data can be checked with:

```bash
python scripts/verify_processed_dataset.py \
  --processed-dir data/processed_full \
  --manifest checksums/processed_full_files.sha256 \
  --split-dir data
```

## Data integrity audit

The processed directory used to create this archive was audited on **August 6,
2026**. The audit found:

- 61,085 processed `.pt` files;
- 0 zero-byte files;
- 0 missing files;
- 0 extra files;
- 0 SHA-256 mismatches against the per-file release manifest;
- disjoint train, validation, and test splits;
- complete split coverage of all 61,085 processed targets;
- no ZIP-container integrity errors in the serialized target files;
- all required target-level key markers present.

The per-file manifest and detailed audit record are maintained in the
companion source-code release. The archive-level checksum in this repository
must be regenerated whenever `processed_full.tar` is replaced.

## Licensing and citation

This repository contains the processed target-level dataset used by the
DuoPose project.

When using this dataset, please cite the associated DuoPose paper and the
original DecoyDB dataset. The processed archive is a project-derived
reconstruction and should not be presented as the complete upstream DecoyDB
dataset.

The dataset-card metadata currently uses `license: other`. Update this field
when the final license for the processed derivative dataset has been confirmed.