---
library_name: pytorch
tags:
  - chemistry
  - molecular-generation
  - atom-count-conditioning
  - molecular-property-conditioning
  - scaffold-decoration
  - flow-matching
  - qm9
  - geom-drugs
  - chembl3d
  - crossdocked
  - structure-based-drug-design
  - 3d
  - arxiv:2606.07239
---

# Morph: 3D molecular generation and scaffold decoration

Checkpoints for **unconditional 3D molecular generation** on **QM9**,
**GEOM-Drugs** and **ChEMBL3D**, **GEOM-Drugs N-conditional generation**,
**GEOM-Drugs and QM9 logP-conditional generation**,
**GEOM-Drugs scaffold decoration**, and **CrossDocked scaffold decoration in
protein pockets**. The releases include the configurations, vocabularies and
training distributions needed for their inference workflows.
Unconditional generation requires no training dataset. The
[N-conditional model](#n-conditional-generation) takes a requested atom count;
[logP conditioning](#logp-conditional-generation) takes a requested lipophilicity;
[scaffold decoration](#scaffold-decoration) takes a supplied scaffold.

See our [paper](https://arxiv.org/abs/2606.07239), *Generative Molecular Morphing for Flexible-Size Design via Unbalanced Optimal Transport*.

Try the models in the **[Morph Playground](https://huggingface.co/spaces/maltefranke/morph-playground)**: choose an unconditional model or a logP/atom-count target, view generated structures in 2D and 3D, and download SDF/CSV files. The public interface uses ZeroGPU for up to eight molecules per request, defaults to GEOM-Drugs unconditional, and includes interactive playback of all 101 trajectory frames with changing atom counts and bonds. Trajectories can be downloaded as JSON and multi-frame XYZ.

## Unconditional checkpoints

| Dataset | Model name | Hydrogen representation | Completed epochs | Files |
| --- | --- | --- | --- | --- |
| QM9 | `qm9-explicit-h` | Explicit H atoms | 1,000, 1,500 | [Bundle](https://huggingface.co/maltefranke/morph/tree/main/qm9/explicit_h) |
| GEOM-Drugs | `geom-explicit-h` | Explicit H atoms | 50, 100 | [Bundle](https://huggingface.co/maltefranke/morph/tree/main/geom/explicit_h) |
| ChEMBL3D | `chembl3d-explicit-h` | Explicit H atoms | 200, 300, 400 | [Bundle](https://huggingface.co/maltefranke/morph/tree/main/chembl3d/explicit_h) |
| ChEMBL3D | `chembl3d-implicit-h` | Heavy atoms; implicit H | 100, 200, 300 | [Bundle](https://huggingface.co/maltefranke/morph/tree/main/chembl3d/implicit_h) |

Epoch numbers count **completed training epochs**: `epoch_050.ckpt` means 50
completed epochs and stores the zero-based Lightning counter `epoch=49`.
Checkpoints are original training files containing current weights, EMA weights
and optimizer state.

The [catalog](https://huggingface.co/maltefranke/morph/blob/main/catalog.json)
is the source of truth for unconditional model names and epochs. Each entry pins an
immutable release revision so its weights and supporting files stay together.
New catalog entries are discovered without updating the Python package.

## N-conditional generation

The **GEOM Fourier N-conditional model** generates molecules conditioned on a
requested number of atoms, **including explicit hydrogens**. It uses full
fine-tuning of the GEOM explicit-H model and Fourier features for the difference
between target and current atom counts. Inference uses EMA weights by default.

| Model | Fine-tuning | Global step | Checkpoint |
| --- | --- | ---: | --- |
| GEOM Fourier N-conditional | Full model | 346,031 | [final.ckpt](https://huggingface.co/maltefranke/morph/resolve/main/geom/atom_count_fourier_full/final.ckpt) |

The [model bundle](https://huggingface.co/maltefranke/morph/tree/main/geom/atom_count_fourier_full)
includes the checkpoint, portable configuration, vocabulary/statistics assets,
matching runtime and `load_model.py`. Use its
[loading and sampling example](https://huggingface.co/maltefranke/morph/blob/main/geom/atom_count_fourier_full/README.md)
to attach `target_n_atoms` to each initial graph. This model has its own loader;
the shared `load_pretrained` interface and catalog below cover unconditional models.

```bash
hf download maltefranke/morph --include 'geom/atom_count_fourier_full/*' --local-dir morph-models
```

The starting atom count for training and evaluation is sampled uniformly from
**3–120**, independently of the requested target count. Empirical GEOM target
counts span **3–71**. Sampling uses 100 integration steps and allows up to
192 atoms; the generated count can differ from the requested count.

## logP-conditional generation

The **GEOM-Drugs and QM9 logP models** generate molecules conditioned on an
absolute **RDKit Crippen logP** target. Both use full fine-tuning with a learned
graph conditioning embedding, explicit hydrogens and EMA inference weights.
The starting atom count is sampled independently of target logP.

| Model | Training status | Initial atom-count prior | Checkpoint and usage |
| --- | --- | --- | --- |
| GEOM logP | Final time-limited save, step 344,733 | Uniform 3–76 | [Checkpoint](https://huggingface.co/maltefranke/morph/resolve/main/geom/logp_full/final.ckpt) · [Bundle and example](https://huggingface.co/maltefranke/morph/blob/main/geom/logp_full/README.md) |
| QM9 logP | 500 completed fine-tuning epochs, step 48,500 | Uniform 3–34 | [Checkpoint](https://huggingface.co/maltefranke/morph/resolve/main/qm9/logp_full/final.ckpt) · [Bundle and example](https://huggingface.co/maltefranke/morph/blob/main/qm9/logp_full/README.md) |

Each bundle includes the exact evaluated checkpoint, portable configuration,
vocabulary/statistics assets, matching runtime and a dataset-free `load_model.py`
loader. Attach a scalar `target_logp` to every initial graph before sampling.
Requested and generated logP can differ; see the parity plots and evaluation
results below. These conditional models use their dedicated bundle loaders.

```bash
hf download maltefranke/morph --include 'geom/logp_full/*' --local-dir morph-models
hf download maltefranke/morph --include 'qm9/logp_full/*' --local-dir morph-models
```

## Scaffold decoration

**GEOM-Drugs scaffold decoration is also available:** the
[`geom-scaffold-murcko` bundle](https://huggingface.co/maltefranke/morph/tree/main/geom/scaffold_murcko_substituent_lora_r16)
contains the checkpoint after **25 fine-tuning epochs**, initialized from the
GEOM explicit-H epoch-100 EMA weights. It uses **rank-16 LoRA**, trainable output
heads and substituent-constrained insertion supervision.

| Model | Hydrogen representation | Completed fine-tuning epochs | Checkpoint |
| --- | --- | ---: | --- |
| `geom-scaffold-murcko` | Explicit H atoms | 25 | [epoch_025.ckpt](https://huggingface.co/maltefranke/morph/resolve/main/geom/scaffold_murcko_substituent_lora_r16/epoch_025.ckpt) |

The model generates decorations conditioned on a Murcko scaffold. Scaffold atom
identities, internal bonds and charges are preserved; scaffold positions can move.
This conditional checkpoint requires the scaffold bundle's saved configuration and
runtime. It is loaded through that workflow, outside the unconditional `morph generate`
interface and catalog.

The bundle includes the original checkpoint, training configuration and source,
portable configuration, vocabularies, distributions, evaluation runtime and fixed
validation/test scaffold panels. See its [usage and reproduction instructions](https://huggingface.co/maltefranke/morph/blob/main/geom/scaffold_murcko_substituent_lora_r16/README.md).


### Scaffold decoration in a protein pocket

[`crossdocked-scaffold`](https://huggingface.co/maltefranke/morph/tree/main/crossdocked/scaffold)
is the evaluated CrossDocked checkpoint after **100 fine-tuning epochs**. It uses
implicit ligand hydrogens, rank-8 LoRA and 10% in-vacuum ChEMBL3D replay.

| Model | Hydrogen representation | Completed fine-tuning epochs | Checkpoint |
| --- | --- | ---: | --- |
| `crossdocked-scaffold` | Heavy atoms; implicit H | 100 | [epoch_100.ckpt](https://huggingface.co/maltefranke/morph/resolve/main/crossdocked/scaffold/epoch_100.ckpt) |

The bundle contains the evaluated checkpoint, portable configuration,
vocabulary/statistics assets and complete metrics table.
Use the [pocket evaluation workflow](https://huggingface.co/maltefranke/morph/blob/main/crossdocked/scaffold/README.md)
with a scaffold and protein pocket. Like the GEOM scaffold model, it is separate
from the unconditional CLI/catalog.

## Evaluation results

Values are **mean ± sample standard deviation** across seeds **42, 43 and 44**
(`ddof=1`). Rates are percentages; diversity and similarity are on a 0–1 scale.
Vina scores are in kcal/mol.
All evaluations use EMA weights and 100 integration steps.

<details>
<summary><strong>Unconditional generation — all 10 checkpoints</strong></summary>

**10,000 generation attempts per seed and checkpoint** (30,000 per checkpoint).
PoseBusters uses the full offline `mol` checks, including flatness and internal energy,
on the generated structures.

| Model | Completed epochs | Novelty (%) | PoseBusters validity (%) | Diversity (0–1) | Uniqueness (%) | Connected-sanitization validity (%) |
| --- | ---: | ---: | ---: | ---: | ---: | ---: |
| QM9 · explicit H | 1,000 | 24.99 ± 0.50 | 93.19 ± 0.30 | 0.8969 ± 0.0003 | 94.77 ± 0.19 | 98.99 ± 0.14 |
| QM9 · explicit H | 1,500 | 22.96 ± 0.23 | 93.53 ± 0.19 | 0.8966 ± 0.0001 | 94.91 ± 0.11 | 99.14 ± 0.08 |
| GEOM-Drugs · explicit H | 50 | 99.77 ± 0.02 | 88.21 ± 0.39 | 0.8855 ± 0.0004 | 100.00 ± 0.00 | 95.22 ± 0.35 |
| GEOM-Drugs · explicit H | 100 | 99.75 ± 0.07 | 88.30 ± 0.24 | 0.8848 ± 0.0001 | 100.00 ± 0.00 | 95.46 ± 0.31 |
| ChEMBL3D · explicit H | 200 | 99.69 ± 0.05 | 91.46 ± 0.24 | 0.8953 ± 0.0002 | 99.99 ± 0.02 | 95.80 ± 0.17 |
| ChEMBL3D · explicit H | 300 | 99.75 ± 0.05 | 91.38 ± 0.16 | 0.8956 ± 0.0005 | 100.00 ± 0.00 | 95.72 ± 0.15 |
| ChEMBL3D · explicit H | 400 | 99.73 ± 0.02 | 91.21 ± 0.37 | 0.8953 ± 0.0001 | 100.00 ± 0.00 | 95.41 ± 0.16 |
| ChEMBL3D · implicit H | 100 | 99.72 ± 0.04 | 94.61 ± 0.43 | 0.9141 ± 0.0002 | 100.00 ± 0.00 | 98.60 ± 0.08 |
| ChEMBL3D · implicit H | 200 | 99.64 ± 0.10 | 95.27 ± 0.16 | 0.9140 ± 0.0002 | 100.00 ± 0.01 | 98.87 ± 0.18 |
| ChEMBL3D · implicit H | 300 | 99.61 ± 0.04 | 95.32 ± 0.09 | 0.9142 ± 0.0001 | 100.00 ± 0.00 | 98.80 ± 0.07 |

Novelty is measured against each checkpoint's training split. Novelty and uniqueness
use canonical, H-stripped molecular identities with stereochemistry ignored.

</details>

<details>
<summary><strong>Scaffold decoration — GEOM-Drugs, 25 fine-tuning epochs</strong></summary>

**512 held-out test scaffolds × 8 generation attempts per scaffold per seed**
(4,096 per seed; 12,288 total), using the fixed released Murcko scaffold panel.
The generated coordinates are not relaxed.

| Model | Completed epochs | Novelty (%) | PoseBusters validity (%) | Diversity (0–1) | Uniqueness (%) | Connected-sanitization validity (%) |
| --- | ---: | ---: | ---: | ---: | ---: | ---: |
| `geom-scaffold-murcko` | 25 (fine-tuning) | 97.52 ± 0.10 | 94.10 ± 0.19 | 0.9076 ± 0.0004 | 63.54 ± 0.59 | 97.99 ± 0.20 |

**PoseBusters protocol:** the native scaffold evaluation checks chemistry and distance
geometry, excluding the full offline energy and flatness checks. The value above is
`posebusters/validity`, the strict all-check pass rate for that configuration; it does
not apply the scaffold-aware inherited-clash exemption. It is therefore **not directly
comparable** to the full offline PoseBusters scores in the unconditional table.
Novelty, diversity and uniqueness describe the pooled generated molecules within each
seed, across all 512 scaffolds; uniqueness is not averaged per scaffold.

See the [scaffold bundle documentation](https://huggingface.co/maltefranke/morph/blob/main/geom/scaffold_murcko_substituent_lora_r16/README.md)
for the saved evaluation runtime and test panel.

</details>

<details>
<summary><strong>Scaffold decoration in a pocket — CrossDocked, 100 fine-tuning epochs</strong></summary>

`crossdocked-scaffold`, evaluated on the published DiffDec single- and multi-decoration
test panels, with **100 attempts per condition per seed** (43,500 total).

| Panel | Conditions | Attempts per seed | Validity (%) | Uniqueness (%) | Similarity (0–1) | Recovery (%) | Vina score (kcal/mol) | High affinity (%) |
| --- | ---: | ---: | ---: | ---: | ---: | ---: | ---: | ---: |
| Single | 43 | 4300 | 88.72 ± 0.71 | 53.32 ± 1.02 | 0.8157 ± 0.0042 | 39.00 ± 1.15 | -8.041 ± 0.007 | 44.18 ± 3.67 |
| Multi | 102 | 10200 | 89.80 ± 0.16 | 66.43 ± 0.38 | 0.7671 ± 0.0032 | 37.83 ± 5.33 | -8.088 ± 0.010 | 40.33 ± 3.14 |

Mean ± sample standard deviation across seeds 42/43/44. The six columns use
DiffDec's grouping and formulas; validity and uniqueness have different definitions
from the unconditional and GEOM scaffold tables. Docking re-embeds and UFF-minimizes
prediction SMILES, so it does not score the original generated coordinates.
Invalid predictions are replaced with the scaffold for docking. Reproduction
differences include reconstructed reference grouping strings, predicted bond
topology, newer RDKit and deterministic embedding seeds.

See the [full CSV, including individual seeds and docking failures](https://huggingface.co/maltefranke/morph/blob/main/crossdocked/scaffold/summary.csv)
and [protocol and reproduction notes](https://huggingface.co/maltefranke/morph/blob/main/crossdocked/scaffold/README.md).

</details>

<details>
<summary><strong>N-conditional generation — GEOM Fourier, results pending publication</strong></summary>

The evaluation covers **90,000 generation attempts** across seeds **42, 43 and 44**:

| Conditioning target N | Attempts per seed | Seeds | Total attempts |
| --- | ---: | --- | ---: |
| Sampled from the empirical GEOM distribution | 10,000 | 42, 43, 44 | 30,000 |
| Fixed N = 80, 90, 100, 110 (each) | 5,000 | 42, 43, 44 | 15,000 per target |

Every panel uses the **uniform 3–120 starting prior**, EMA weights and 100
integration steps. The fixed targets exceed the empirical support and measure
count extrapolation. Scoring includes atom-count accuracy, validity, uniqueness,
novelty, stability, PoseBusters and energies, with means and sample standard
deviations across seeds. Numerical results have not yet been published.

See the [N-conditional bundle documentation](https://huggingface.co/maltefranke/morph/blob/main/geom/atom_count_fourier_full/README.md)
for the model and sampling protocol.

</details>

<details open>
<summary><strong>logP-conditional generation — GEOM-Drugs and QM9</strong></summary>

**10,000 distinct held-out test molecules per dataset**, sampled once without
replacement and reused as logP targets for seeds **42, 43 and 44**: **30,000
attempts per model, 60,000 total**. Sampling uses EMA weights, 100 steps and
bfloat16, with each model's training atom-count prior from the table above.

| Model | logP MAE | logP RMSE | Pearson r | Scorable (%) | Validity (%) | PoseBusters (%) |
| --- | ---: | ---: | ---: | ---: | ---: | ---: |
| GEOM-Drugs | 0.538 ± 0.001 | 0.690 ± 0.002 | 0.868 ± 0.002 | 95.96 ± 0.14 | 92.02 ± 0.09 | 87.14 ± 0.21 |
| QM9 | 0.304 ± 0.002 | 0.380 ± 0.003 | 0.917 ± 0.002 | 98.73 ± 0.05 | 98.23 ± 0.07 | 92.83 ± 0.18 |

Mean ± sample standard deviation across the three seeds. LogP errors and
correlation use scorable sanitized outputs; coverage, validity and PoseBusters
rates use all attempts. Failed outputs are retained and are not resampled.
PoseBusters uses the full offline molecule checks. The CSVs also report logP
errors restricted to chemically valid outputs, tolerance success rates, novelty,
diversity, uniqueness, stability and energies.

- GEOM: [all metrics](https://huggingface.co/maltefranke/morph/blob/main/geom/logp_full/summary.csv) · [logP parity](https://huggingface.co/maltefranke/morph/resolve/main/geom/logp_full/logp_parity.png) · [atom count vs. logP](https://huggingface.co/maltefranke/morph/resolve/main/geom/logp_full/natoms_logp.png)
- QM9: [all metrics](https://huggingface.co/maltefranke/morph/blob/main/qm9/logp_full/summary.csv) · [logP parity](https://huggingface.co/maltefranke/morph/resolve/main/qm9/logp_full/logp_parity.png) · [atom count vs. logP](https://huggingface.co/maltefranke/morph/resolve/main/qm9/logp_full/natoms_logp.png)

</details>

Metric definitions for the unconditional and GEOM scaffold tables:

- **Novelty:** fraction of generated, SMILES-convertible molecules absent from the
  corresponding training reference, retaining duplicates in the denominator.
- **PoseBusters validity:** fraction of all generation attempts passing every enabled
  PoseBusters check; missing checks and failed molecule conversions count as failures.
- **Diversity:** mean pairwise Tanimoto distance between ECFP6 fingerprints (radius 3,
  2,048 bits), retaining duplicates among fingerprintable molecules.
- **Uniqueness:** distinct canonical molecular identities divided by all
  SMILES-convertible molecules within a seed.
- **Connected-sanitization validity:** fraction of all attempts producing connected,
  RDKit-sanitizable molecules, with no additional charge or radical restriction
  (`connected-sanitization-validity`).

## Install and authenticate

The runtime bundled with GEOM includes the shared inference interface and catalog
discovery for all models in the unconditional checkpoint table. Install it in a Python environment:

```bash
python -m pip install 'huggingface_hub>=1.0,<2'
hf auth login
hf download maltefranke/morph \
  --local-dir morph-runtime \
  --include 'geom/explicit_h/runtime/*'
python -m pip install ./morph-runtime/geom/explicit_h/runtime
morph models list
```

Authentication requires an account with access to this model repository.
Python >=3.11 and a compatible PyTorch 2.9 installation are required; CUDA
generation needs a CUDA-capable PyTorch build. Dependency constraints and tested
versions are included in the runtime. An existing Morph environment with catalog
discovery can use the examples directly. On a cluster, run model loading and
generation inside a compute allocation.

## Load and generate from Python

```python
from morph.inference import load_pretrained

model = load_pretrained("geom-explicit-h", epoch=100, device="cuda")

trajectories, validity, vocab, coordinate_std = model.generate(
    n_mols=64,
    batch_size=16,
    seed=42,
    return_traj=False,
)
```

Use `device="cpu"` for CPU inference. The same interface loads QM9 and ChEMBL3D:

```python
model = load_pretrained("qm9-explicit-h", epoch=1500, device="cuda")
# Use epoch=1000 for the earlier QM9 checkpoint.
model = load_pretrained("chembl3d-explicit-h", epoch=400, device="cuda")
# Or choose the heavy-atom model:
model = load_pretrained("chembl3d-implicit-h", epoch=300, device="cuda")
```

Loading downloads only the selected checkpoint and its six required asset files,
then reuses the Hugging Face cache. Each checkpoint is approximately 510 MB.
Omitting `epoch` selects the latest published epoch for the chosen model.

**EMA weights are used by default.** The inference loader constructs one network
and loads only the selected weights; the unselected network and optimizer tensors
are not materialized from the memory-mapped checkpoint. To select current
training weights, pass `use_ema=False` to `load_pretrained`.

Generation defaults to 100 integration steps. Each attempt remains in the returned
lists, including invalid molecules. `trajectories[i][-1]` is its final graph and
`validity[i]` is its RDKit validity result. Pass `return_traj=True` to retain all
integration frames. Returned coordinates are already in **Angstrom**; do not
multiply them by `coordinate_std` again.

## CLI and downloads

Generate GEOM-Drugs molecules and save valid structures to SDF:

```bash
morph generate --model geom-explicit-h --epoch 100 \
  --device cuda --batch 64 --batch-size 16 --output geom-e100.sdf
```

`--batch` is the total number of generation attempts. Add `--save-trajs` to save
trajectories and validity flags beside the SDF. `--no-ema` selects current
weights, and `--steps` and `--seed` control sampling.

Download a portable bundle without loading the model:

```bash
morph models download geom-explicit-h --epoch 100 --local-dir morph-models
```

Load that local bundle without network access or catalog lookup:

```python
model = load_pretrained(
    weights_dir="morph-models/geom/explicit_h",
    epoch=100,
    device="cuda",
)
```

Use another model name from the table for QM9 or ChEMBL3D. The interface also accepts
direct Hub repository IDs with `subfolder` and `revision`. Pass `cache_dir` to
choose a cache location, or `local_files_only=True` to load a previously cached
release without network access. The corresponding CLI options are `--cache-dir`
and `--offline`.

The root `sample.py` remains available as the archived ChEMBL3D sampling entry
point with its corresponding bundled runtimes. The shared interface above
supports all four published unconditional model variants.

## Reproducibility

Each unconditional model has a **training bundle** with its original training configuration,
launch settings, frozen source, dependency versions and data references.
The bundles live alongside the corresponding model files on Hugging Face:

| Model | Configuration and source |
| --- | --- |
| QM9, explicit H | [Training bundle](https://huggingface.co/maltefranke/morph/resolve/main/qm9/explicit_h/training_bundle.tar.gz) |
| GEOM-Drugs, explicit H | [Training bundle](https://huggingface.co/maltefranke/morph/resolve/main/geom/explicit_h/training_bundle.tar.gz) |
| ChEMBL3D, explicit H | [Training bundle](https://huggingface.co/maltefranke/morph/resolve/main/chembl3d/explicit_h/training_bundle.tar.gz) |
| ChEMBL3D, implicit H | [Training bundle](https://huggingface.co/maltefranke/morph/resolve/main/chembl3d/implicit_h/training_bundle.tar.gz) |

For example, download and unpack the GEOM training bundle:

```bash
hf download maltefranke/morph geom/explicit_h/training_bundle.tar.gz --local-dir .
tar -xzf geom/explicit_h/training_bundle.tar.gz
```

Each bundle also includes a portable training recipe; its changes from the
original run are recorded alongside it. See the [training bundle guide](reproduction/README.md)
for setup, data requirements and resuming. Version identifiers and checksums
are recorded in the release metadata and bundle manifests.

## Hydrogen handling and training data

| Dataset | Training filter | Representation |
| --- | --- | --- |
| QM9 | Original QM9 split | Explicit-H variant |
| GEOM-Drugs | At most 72 total atoms, including H | Explicit-H variant |
| ChEMBL3D | At most 100 total atoms, including H before hydrogen removal | Explicit-H and implicit-H variants |

Both ChEMBL3D variants use the same split membership: 1,474,008 training,
184,295 validation and 184,242 test molecules. The implicit-H model predicts
heavy atoms, bonds and formal charges; RDKit infers hydrogens during conversion
and validity checking. Its geometry statistics and coordinate normalization were
recomputed for the heavy-atom representation.

QM9 uses 100,000 training, 20,000 validation and 12,947 test molecules. Its run
started from scratch, validates every epoch and retains checkpoints every 25 epochs.

The GEOM-Drugs epoch 50 and 100 checkpoints come from the run resumed from the
best checkpoint after 48 completed epochs. The resumed run validates every epoch and retains
checkpoints every fifth epoch. The published unconditional epochs are listed in the catalog. The scaffold-decoration
checkpoint adds 25 fine-tuning epochs on top of the epoch-100 model.

Explicit-H models generate H atoms as graph vertices. Every model uses its own
ordered vocabularies, distributions and saved configuration; keep those files
together. The training atom limits describe dataset filtering and do not impose
a hard atom-count limit during generation.

## Bundle contents and verification

Each unconditional model directory contains:

- `epoch_NNN.ckpt`: the published original training checkpoints.
- `config.yaml`: portable inference configuration with dataset loading disabled.
- `atom_tokens.txt`, `edge_tokens.txt`, `charge_tokens.txt`: ordered vocabularies.
- `distributions.pt`: original training statistics, including atom counts,
  coordinate scale, type frequencies and geometry histograms.
- `distributions.json`: a readable export of those statistics.
- `metadata.json`: dataset, hydrogen mode, epochs, optimizer steps, original
  filenames and checkpoint SHA-256 hashes.
- `training_config.yaml`: the original training configuration for provenance.
- `runtime/`: archived source, configs and dependency specifications.
- `training_bundle.tar.gz`: original run configuration, source, environment and a
  portable training recipe; `training_bundle.json` records its checksum and provenance.
- `validation.json`: results of checkpoint loading and sampling checks.

The published unconditional bundles were checked for strict current/EMA weight loading
and end-to-end sampling. These functionality checks are separate from the
[generation benchmarks above](#evaluation-results).
The GEOM and QM9 releases also passed remote file-size and hash verification.

The [ChEMBL3D release manifest](https://huggingface.co/maltefranke/morph/blob/main/manifest.json)
describes the original ChEMBL3D release, including its historical model card.
[QM9](https://huggingface.co/maltefranke/morph/blob/main/qm9/explicit_h/manifest.json) and
[GEOM-Drugs](https://huggingface.co/maltefranke/morph/blob/main/geom/explicit_h/manifest.json)
have their own bundle manifests.
Original training configs contain historical cluster paths; use `config.yaml`
through the inference loader for portable generation.

## Citation

If you use Morph, please cite our [paper](https://arxiv.org/abs/2606.07239):

```bibtex
@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},
}
```

Machine-readable citation metadata is available in [CITATION.cff](CITATION.cff).