maltefranke/morph-data

Morph data

Ready-to-use processed QM9, GEOM-Drugs, and ChEMBL3D data for Morph. Download the original processed files directly, including train/validation/test splits, ordered vocabularies, training statistics, and SMILES sidecars. No source-data download or preprocessing is needed to use these caches with Morph.

This dataset repository is public and supports unauthenticated downloads. It belongs to the Morph collection.

Available datasets

Sizes are decimal GB. GEOM counts are conformer records; QM9 and ChEMBL3D counts are molecule records.

Directory	Representation / selection	Train	Validation	Test	Size
qm9	Explicit H; original Morph QM9 splits	100,000	20,000	12,947	0.326 GB
geom	GEOM-Drugs, explicit H; at most 72 total atoms	5,645,654	703,830	706,568	56.170 GB
chembl3d	Explicit H; processed cache without the 100-atom cap	1,479,308	184,947	184,960	14.795 GB
chembl3d_max100	Explicit H; at most 100 total atoms	1,474,008	184,295	184,242	14.726 GB
chembl3d_max100_implicit_h	Implicit H; same capped source molecules	1,474,008	184,295	184,242	9.925 GB

The ChEMBL3D 100-atom cap counts hydrogens before removal. The implicit-H cache has its own vocabularies/statistics and includes the charge/bond normalization used for the released implicit-H models.

Total processed data: 95,942,750,644 bytes (95.94 GB) in 61 files. Scaffold-decoration and protein-pocket datasets are not included in this release.

Download

From a Morph checkout, download a selected directory into data/:

# Choose one or more datasets:
hf download maltefranke/morph-data --type dataset --include 'qm9/**' --local-dir data
hf download maltefranke/morph-data --type dataset --include 'geom/**' --local-dir data
hf download maltefranke/morph-data --type dataset --include 'chembl3d_max100/**' --local-dir data
hf download maltefranke/morph-data --type dataset --include 'chembl3d_max100_implicit_h/**' --local-dir data

# Uncapped ChEMBL3D cache:
hf download maltefranke/morph-data --type dataset --include 'chembl3d/**' --local-dir data

For example, QM9 will be available as data/qm9/processed/train.pt, and GEOM as data/geom/processed/train_data.lmdb/data.mdb.

Download everything, including the manifest and checksums:

hf download maltefranke/morph-data --type dataset --local-dir data
(cd data && sha256sum -c SHA256SUMS)

Or use Python:

from huggingface_hub import snapshot_download

snapshot_download(
    repo_id="maltefranke/morph-data",
    repo_type="dataset",
    allow_patterns=["qm9/**"],
    local_dir="data",
)

For a reproducible download, pass a dataset commit hash with --revision in the CLI or revision= in Python. These are native Morph caches; load them with Morph's dataset classes rather than datasets.load_dataset.

Use a Morph checkout with support for loading processed QM9 caches without raw inputs. Older QM9 loaders, including those preserved in historical training bundles, may still fetch raw inputs during dataset initialization.

Matching the released checkpoints

Model subfolder in maltefranke/morph	Dataset directory here
qm9/explicit_h	qm9
geom/explicit_h	geom
chembl3d/explicit_h	chembl3d_max100
chembl3d/implicit_h	chembl3d_max100_implicit_h

When using a model's training bundle, point MORPH_DATASET_ROOT to the downloaded dataset directory that contains processed/. For example:

export MORPH_DATASET_ROOT="$PWD/data/chembl3d_max100_implicit_h"

Vocabulary/statistics SHA-256 hashes and split-file sizes were checked against the dataset references in the published training bundles. Those historical references contain split sizes, not split content hashes. This dataset snapshot adds full SHA-256 checksums for every distributed data file in SHA256SUMS and manifest.json.

File layout

Each dataset directory contains a processed/ subdirectory with:

• QM9: train.pt, val.pt, test.pt, plus PyG preprocessing metadata.
• GEOM and ChEMBL3D: train_data.lmdb/data.mdb, val_data.lmdb/data.mdb, test_data.lmdb/data.mdb.
• All datasets: atom_tokens.txt, edge_tokens.txt, charge_tokens.txt, distributions.pt, and train_smiles.txt, val_smiles.txt, test_smiles.txt.
• ChEMBL3D: original preprocessing_summary.json; the capped variants also contain *_source_indices.npy mappings to their source-cache records.

LMDB records contain atomic numbers (z), coordinates (pos), charges, edge_index, edge_attr, and SMILES. Coordinates are stored in physical Angstroms; Morph applies the supplied training statistics when constructing model inputs. SMILES sidecars contain unique split SMILES and are not per-record index maps. Keep each cache's split files, vocabularies, and statistics together.

Only the LMDB data.mdb files are distributed; the Morph reader uses read-only LMDB access without lock files. Raw inputs, alternative statistics backups, scaffold caches, and temporary files are omitted.

Sources and citation

These are processed snapshots of existing molecular datasets. Upstream sources used by Morph's preprocessing include the corrected QM9 structures, the GEOM-Drugs Revisited split files, and LoQI's ChEMBL3D processed release (Figshare file IDs 62280814, 62280805, 62280802). Consult the upstream releases for their dataset licenses and citation details.

For Morph, please cite Generative Molecular Morphing for Flexible-Size Design via Unbalanced Optimal Transport:

@misc{franke2026generativemolecularmorphingflexiblesize,
  title={Generative Molecular Morphing for Flexible-Size Design via Unbalanced Optimal Transport},
  author={Malte Franke and Stefan P. Schmid and Zarko Ivkovic and Kjell Jorner and Andreas Krause},
  year={2026},
  eprint={2606.07239},
  archivePrefix={arXiv},
  primaryClass={cs.LG},
  url={https://arxiv.org/abs/2606.07239},
}