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---
license: cc-by-sa-4.0
pretty_name: 3DCS baseline embeddings
tags:
- chemistry
- molecules
- 3d
- conformers
- embeddings
- benchmark
size_categories:
- 10M<n<100M
viewer: false
---
# 3DCS baseline embeddings
This repository holds the embedding files of the baseline molecular representations evaluated in the ICLR 2026 paper
*3DCS: Datasets and Benchmark for Evaluating Conformational Sensitivity in Molecular Representations*. It also holds the
metric outputs of the original evaluation runs and the rMD17 split files.
The files are the original bytes produced in the authors' 2025 runs. Only directory names were normalized. File names,
array keys and contents are unchanged, and every file's sha256 is listed in [`manifest.csv`](manifest.csv).
- **Paper (ICLR 2026):** https://openreview.net/forum?id=JAb0y8lkqL
- **Code (evaluation toolkit, Python module `three_dbench`):** https://github.com/ComDec/3DCS
- **Conformer datasets:** [`EscheWang/3dcs`](https://huggingface.co/datasets/EscheWang/3dcs) (configs `chirality`,
`rotation`, `traj_frames`, `traj_energies`). The embedding rows here are aligned with the rows of these configs (see
[Alignment](#alignment-with-the-conformer-datasets)).
## Contents
139 data files (21.16 GB) plus `README.md` and `manifest.csv`.
| Directory | Contents | Files | Size | Paper |
|---|---|---|---|---|
| `chirality/<model>/` | Embeddings of the 52,391 chirality conformers, 7 models | 7 | 382 MB | Table 2 |
| `chirality_legacy_15218/` | Embeddings of an older 15,218-conformer chirality set (3,978 molecules), 5 models | 6 | 205 MB | Table 4 |
| `traj/<model>/` | Embeddings of the rMD17 frames (10 molecules), 7 models | 70 | 7.10 GB | Tables 3, 6 |
| `rotation/gemnet/` | GemNet embeddings of all 10,097,643 rotation conformers (16 shards) | 16 | 5.17 GB | Table 1 |
| `rotation/{fmg,mace}/` | FMG and MACE embeddings of rotation shard 0 only | 2 | 904 MB | not used in the paper |
| `results/chirality/` | Per-molecule and summary outputs of the original chirality runs | 7 | 6.8 MB | Tables 2, 4 |
| `results/traj/` | Per-window and summary outputs of the original energy runs | 16 | 12.0 MB | Tables 3, 6, 7 |
| `results/rotation/` | Per-molecule and summary outputs of the original geometry runs | 5 | 7.38 GB | Table 1 |
| `splits/rmd17/` | Official rMD17 train/test index files, splits 01–05 | 10 | 59 KB | Tables 8, 9 (split 01) |
```
README.md
manifest.csv
chirality/
e3fp/sampled_chi.pkl
gemnet/sampled_feature.npz
molae/1.npz
molspectra/sampled_mol_feature.npz
unimol/1.npz
fmg/chirality_bench_conformers_noised_only_aslist_embed.npz
mace/chirality.npz
chirality_legacy_15218/
molae/0.npz unimol/0.npz gemnet/chirality_gemnet128.npz molspectra/mol_feature.npz
e3fp/chi.pkl chirality_embedding_dict.pkl
traj/{e3fp,gemnet,molae,molspectra,unimol,fmg,mace}/rmd17_<molecule>.{npz,pkl}
rotation/
gemnet/rotation_conformers_{0..15}.npz
fmg/rot_mol_list_0_embed.npz
mace/rot0.npz
results/
chirality/en_sep_results/{e3fp,gemnet,molae,molspectra,unimol}.json, summary.csv
chirality/chirality_metrics_summary.csv
traj/energy_metrics_out/{config.json, summary_overall.csv, details_long.csv, details_rmd17_<molecule>.csv}
traj/energy_metrics_out_fix_ckf/{config.json, summary_overall.csv}
traj/traj_e.csv
rotation/{all_metric.csv, model_metric_summary.csv, summary.parquet, metrics_all_0.1_1.json.gz, metrics_sup_100.json.gz}
splits/rmd17/index_{train,test}_0{1..5}.csv
```
`<molecule>` is one of `aspirin`, `azobenzene`, `benzene`, `ethanol`, `malonaldehyde`, `naphthalene`, `paracetamol`,
`salicylic`, `toluene`, `uracil`.
## File formats
All `.npz` files hold `float32` embedding matrices with one row per conformer. The array key differs between models, so
pass it explicitly (for example `--embedding-key` in the toolkit CLI). E3FP fingerprints are pickled lists of RDKit
`ExplicitBitVect` objects and need RDKit to load. No embedding array contains NaN or Inf values.
### `chirality/` (Table 2)
| Model | File | Key | Shape | dtype |
|---|---|---|---|---|
| E3FP | `chirality/e3fp/sampled_chi.pkl` | dict key `e3fp` | list of 52,391 `ExplicitBitVect` (1,024 bits) | RDKit bit vector |
| GemNet | `chirality/gemnet/sampled_feature.npz` | `gemnet` | (52391, 128) | float32 |
| MolAE | `chirality/molae/1.npz` | `arr_0` | (52391, 512) | float32 |
| MolSpectra | `chirality/molspectra/sampled_mol_feature.npz` | `arr_0` | (52391, 256) | float32 |
| UniMol | `chirality/unimol/1.npz` | `arr_0` | (52391, 512) | float32 |
| FMG | `chirality/fmg/chirality_bench_conformers_noised_only_aslist_embed.npz` | `embeddings` | (52391, 128) | float32 |
| MACE | `chirality/mace/chirality.npz` | `arr_0` | (52391, 256) | float32 |
- `sampled_chi.pkl` also has a `morgan` key (52,391 Morgan `ExplicitBitVect`, 2,048 bits), which Table 2 does not use.
- The FMG file also has a `smiles` key (one SMILES string per row).
- In `mace/chirality.npz`, 17 rows have an L2 norm above 10 times the median row norm (median 1.03, maximum about
1.5e4). The file is published as it was used.
### `traj/` (Tables 3 and 6)
Each model directory has one file per rMD17 molecule. Each file has 100,000 rows, except `rmd17_azobenzene`, which has
99,988.
| Model | Directory | Key | Dim | dtype |
|---|---|---|---|---|
| E3FP | `traj/e3fp/rmd17_<molecule>.pkl` | pickled list | 1,024 bits | RDKit `ExplicitBitVect` |
| GemNet | `traj/gemnet/rmd17_<molecule>.npz` | `gemnet` | 128 | float32 |
| MolAE | `traj/molae/rmd17_<molecule>.npz` | `arr_0` | 512 | float32 |
| MolSpectra | `traj/molspectra/rmd17_<molecule>.npz` | `arr_0` | 256 | float32 |
| UniMol | `traj/unimol/rmd17_<molecule>.npz` | `arr_0` | 512 | float32 |
| FMG | `traj/fmg/rmd17_<molecule>.npz` | `embeddings` (also `smiles`) | 128 | float32 |
| MACE | `traj/mace/rmd17_<molecule>.npz` | `arr_0` | 256 | float32 |
### `rotation/` (Table 1)
| Model | Files | Key | Rows | Dim | dtype |
|---|---|---|---|---|---|
| GemNet | `rotation/gemnet/rotation_conformers_{0..15}.npz` (file index = `shard`) | `gemnet` | 627,436–633,781 per shard; 10,097,643 in total | 128 | float32 |
| FMG | `rotation/fmg/rot_mol_list_0_embed.npz` (shard 0 only) | `embeddings` (also `smiles`) | 630,021 | 128 | float32 |
| MACE | `rotation/mace/rot0.npz` (shard 0 only) | `arr_0` | 630,021 | 256 | float32 |
This repository holds rotation embeddings for GemNet (all 16 shards) and for FMG and MACE (shard 0). The per-molecule
metrics of all five Table 1 models are in `results/rotation/`.
### `chirality_legacy_15218/` (Table 4)
This is an older version of the chirality set, with 15,218 conformers of 3,978 molecules and heavy atoms only. It is not
part of `EscheWang/3dcs`.
| File | Content |
|---|---|
| `chirality_embedding_dict.pkl` | dict `{mol_id: {"mol": [RDKit Mol], "e3fp", "morgan", "gemnet", "molae", "molspectra", "unimol", "opd"}}`, 3,978 molecules |
| `molae/0.npz`, `unimol/0.npz` | key `arr_0`, (15218, 512) float32 |
| `gemnet/chirality_gemnet128.npz` | key `gemnet`, (15218, 128) float32 |
| `molspectra/mol_feature.npz` | key `mol_feature`, (15218, 256) float32 |
| `e3fp/chi.pkl` | dict `{"base": {mol_id: [Mol]}, "e3fp": {"fps": {mol_id: [ExplicitBitVect(1024)]}}, "morgan": {"fps": {...}}}` |
## Alignment with the conformer datasets
### Chirality
Rows follow the conformer order of `EscheWang/3dcs` config `chirality`: dataset rows in file order, then `mol_blocks`
in list order. Row `r` of the dataset owns embedding rows `offset : offset + n_conformers`.
```python
import numpy as np
from datasets import load_dataset
from huggingface_hub import hf_hub_download
chir = load_dataset("EscheWang/3dcs", name="chirality", split="train")
path = hf_hub_download("EscheWang/3dcs-embeddings", "chirality/unimol/1.npz", repo_type="dataset")
X = np.load(path)["arr_0"] # (52391, 512)
row = chir[0]
Z = X[row["offset"] : row["offset"] + row["n_conformers"]] # one row per MolBlock in row["mol_blocks"]
```
The per-molecule JSON files in `results/chirality/en_sep_results/` list the 3,903 molecules in the same order as the
`mol_id` values of the `chirality` config.
### Trajectories (rMD17)
Row `i` of `traj/<model>/rmd17_<molecule>.*` is the frame with `frame_idx == i` for `mol_type == "rmd17_<molecule>"` in
config `traj_frames`, and energy `i` in config `traj_energies`. rMD17 frames are not stored in molecular-dynamics time
order, so consecutive rows are not consecutive MD steps.
### Rotation
The GemNet embeddings are stored per shard, and the `offset` column of config `rotation` counts **within a shard**:
- `rotation/gemnet/rotation_conformers_<s>.npz` holds the conformers of all dataset rows with `shard == s`.
- A row with `shard == s` owns rows `offset : offset + n_conformers` of that file.
- The rows of the `rotation` config are grouped by shard in the order 0, 1, 10, 11, …, 15, 2, …, 9, and `offset`
restarts at 0 for each shard. Do not use `offset` as an index into one concatenated array.
```python
import numpy as np
from datasets import load_dataset
from huggingface_hub import hf_hub_download
rot = load_dataset("EscheWang/3dcs", name="rotation", split="train").select_columns(
["key", "shard", "offset", "n_conformers", "torsion_deg"])
_cache = {}
def gemnet_rows(row):
s = row["shard"]
if s not in _cache:
f = hf_hub_download("EscheWang/3dcs-embeddings", f"rotation/gemnet/rotation_conformers_{s}.npz",
repo_type="dataset")
_cache[s] = np.load(f)["gemnet"]
return _cache[s][row["offset"] : row["offset"] + row["n_conformers"]] # aligned with row["torsion_deg"]
```
How this rule was checked:
- **Row counts.** In each shard, the number of rows in `rotation_conformers_<s>.npz` equals the sum of `n_conformers`
over the dataset rows with `shard == s`. The per-shard totals (627,436 to 633,781) are all different, so a file cannot
be matched to the wrong shard without a count mismatch.
- **Source order.** The source files of the 16 shards (`rot_conf_deg_<s>.lmdb`, not included here) list the
same molecules as the `rotation` config, in the same order, with the same `n_conformers` and `torsion_deg`.
- **Molecule boundaries in the embeddings.** Conformers of one molecule are close in GemNet space, so a new molecule
shows up as a jump between consecutive rows. In each of the 16 files, 91–92% of the molecule boundaries given by
`offset` coincide with a jump larger than the 99th percentile of within-molecule steps. With `offset` shifted by 7 rows
in either direction, about 15% coincide.
- **Stored pair distances.** The per-molecule metric files in `results/rotation/` store GemNet distances between
conformer pairs. In `metrics_all_0.1_1.json.gz`, all stored distances of 145,293 of the 146,389 molecules are
reproduced (relative difference ≤ 1e-5) from the rows selected by this rule.
The GitHub toolkit reads these files with `evaluate rotation --layout by-shard`, which applies exactly this rule.
### Older chirality set
Rows of the four 15,218-row `.npz` files follow the key order of `chirality_embedding_dict.pkl`, with each molecule's
conformers in list order. Flattening the dictionary in that order reproduces the four files exactly.
## Download
Download only what you need with `huggingface_hub`:
```python
from huggingface_hub import snapshot_download
# Table 2 embeddings of all 7 models (382 MB)
snapshot_download("EscheWang/3dcs-embeddings", repo_type="dataset", local_dir="3dcs-embeddings",
allow_patterns=["manifest.csv", "chirality/*"])
# One model on the rMD17 trajectories
snapshot_download("EscheWang/3dcs-embeddings", repo_type="dataset", local_dir="3dcs-embeddings",
allow_patterns=["traj/gemnet/*"])
# One rotation shard
snapshot_download("EscheWang/3dcs-embeddings", repo_type="dataset", local_dir="3dcs-embeddings",
allow_patterns=["rotation/gemnet/rotation_conformers_0.npz"])
```
The same with the command line:
```bash
hf download EscheWang/3dcs-embeddings --repo-type dataset --include "chirality/*" --local-dir 3dcs-embeddings
```
The GitHub toolkit provides a download helper for these files, for example
`python -m three_dbench download embeddings --task chirality` (see the
[toolkit README](https://github.com/ComDec/3DCS) for the version that includes it).
Check downloads against the `sha256` column of `manifest.csv`.
## Evaluate
Save the conformer dataset in the format the CLI reads, then pass an embedding file and its key:
```python
from datasets import load_dataset
load_dataset("EscheWang/3dcs", name="chirality", split="train").save_to_disk("data/hf/chirality")
```
```bash
python -m three_dbench evaluate chirality \
--dataset-dir data/hf/chirality \
--embeddings 3dcs-embeddings/chirality/unimol/1.npz \
--embedding-key arr_0 \
--model-name unimol \
--output-dir results/chirality_unimol
```
Use `--embedding-key gemnet` for GemNet and `--embedding-key embeddings` for FMG. See the GitHub repository for the
trajectory and rotation evaluators, E3FP inputs, and the expected values for each table.
## `manifest.csv`
One row per data file (README.md and manifest.csv are not listed). Columns:
| Column | Meaning |
|---|---|
| `path` | Path in this repository |
| `task` | `chirality`, `chirality_legacy_15218`, `traj` or `rotation` |
| `model` | Model name, or the models covered by a results file |
| `size_bytes`, `sha256` | Size and SHA-256 of the file |
| `format` | `npz`, `pickle`, `csv`, `json`, `json.gz` or `parquet` |
| `keys`, `shape`, `dtype` | Array keys with their shapes and dtypes (`;`-separated, in the same order). For pickles, CSV and JSON, a short description of the structure |
| `source_nyubox_path` | Where the file came from in the authors' internal archive, as a label; the internal path itself is not published. The column name is kept so existing scripts keep working |
| `source_mtime` | Modification time of the source file (UTC) |
| `paper_tables` | Paper tables that the file relates to |
| `extraction_status` | The extraction script for this file, where one exists, and how closely a rerun of it agrees with this file. "Full-precision conformers" means the authors' source RDKit molecules; starting from the four-decimal MolBlocks published in `EscheWang/3dcs` the agreement is looser, as `baselines/README.md` describes |
| `notes` | Additional information |
## Provenance and status
- **Origin.** The embeddings were computed by the authors in 2025: in September for E3FP, GemNet, MolAE, MolSpectra
and UniMol, and on 18 November for FMG and MACE. All files except `results/traj/traj_e.csv` were copied from the
authors' backup without modification, and their SHA-1 and size were checked against the backup copy.
`results/traj/traj_e.csv` comes from the authors' paper directory.
- **E3FP fingerprints.** They are produced with `e3fp` 1.2.7 and the parameters below. Recomputing 3,000 random
chirality conformers from the source RDKit molecules gives bit-identical fingerprints; starting from the MolBlocks in
`EscheWang/3dcs`, about 5% of conformers get different bits.
```python
from e3fp.pipeline import fprints_from_mol # e3fp 1.2.7
fp = fprints_from_mol(mol, fprint_params=dict(bits=1024, level=5, radius_multiplier=1.5, stereo=True,
include_disconnected=True, rdkit_invariants=True, first=1, counts=False))[0].to_rdkit()
```
- **Other models.** For each file, the array key, shape and dtype are listed above and in `manifest.csv`; every file
holds one row per conformer in the order described under [Alignment](#alignment-with-the-conformer-datasets).
- **Extraction scripts.** The GitHub repository carries one extraction script per baseline model under
[`baselines/`](https://github.com/ComDec/3DCS/tree/main/baselines), each with a pinned environment, the
upstream commit it drives and the SHA-256 of the weight file it loads; the `extraction_status` column of
`manifest.csv` records, per file, what that script reproduces.
- **Reference values.** `reproduce/` in the GitHub repository recomputes the chirality and energy tables from these
embeddings and compares a run against the reference values of that release.
- **Results files.**
- `results/chirality/en_sep_results/` holds the per-molecule outputs behind Table 2 for E3FP, GemNet, MolAE,
MolSpectra and UniMol. They were computed with Euclidean distance between embeddings. FMG and MACE were not part of
that run.
- `results/chirality/chirality_metrics_summary.csv` holds the summary for the older 15,218-conformer set. Its
`__cosine` columns correspond to the Spearman, Kendall and CKA rows of Table 4.
- `results/traj/traj_e.csv` is the table from which Tables 6 and 7 were taken. It comes from the authors' paper
directory, not from the backup. Its non-EJS columns equal `energy_metrics_out/summary_overall.csv`, and its EJS
columns were recomputed with a multi-λ robust variant.
- `results/traj/energy_metrics_out_fix_ckf/` is an earlier run with the same configuration.
- `results/rotation/metrics_all_0.1_1.json.gz` holds per-molecule metrics from a run on a 10% sample of molecules.
It is the source of the Spearman, Kendall, CKA, isoR² and Torsion-SP cells of Table 1.
- `results/rotation/metrics_sup_100.json.gz` holds per-molecule LIE@k and AS from a run over 1,464,493 molecules. It
is the source of the LIE@k and AS cells of Table 1.
- `all_metric.csv`, `model_metric_summary.csv` (a byte-identical copy) and `summary.parquet` are model-level summaries
of the 10% sample run.
- **Splits.** `splits/rmd17/` holds the official rMD17 split index files, unchanged. The RMD17 fine-tuning inputs in the
authors' backup correspond to split 01.
- **Not included.** Rotation embeddings for E3FP, UniMol, MolAE and MolSpectra, and fine-tuned model checkpoints.
## License
The contents of this repository are released under **CC BY-SA 4.0**.
The embeddings are outputs of third-party models, and this repository does not restate the licenses of those models or
their weights. If you use the embeddings, please cite the original model papers as well as 3DCS:
- E3FP: Axen et al., *A simple representation of three-dimensional molecular structure*, J. Med. Chem. 60(17), 2017.
- GemNet: Gasteiger, Becker and Günnemann, *GemNet: Universal directional graph neural networks for molecules*
(arXiv:2106.08903).
- MolAE: Yang et al., *Mol-AE: Auto-encoder based molecular representation learning with 3D cloze test objective*
(bioRxiv 2024).
- MolSpectra: Wang et al., *MolSpectra: Pre-training 3D molecular representation with multi-modal energy spectra*
(arXiv:2502.16284).
- UniMol: Zhou et al., *Uni-Mol: A universal 3D molecular representation learning framework* (ICLR 2023).
- MACE: Batatia et al., *MACE: Higher order equivariant message passing neural networks for fast and accurate force
fields* (arXiv:2206.07697).
- FMG: Dumitrescu et al., *E(3)-equivariant models cannot learn chirality: Field-based molecular generation* (ICLR 2025).
The conformers come from ChEMBL (CC BY-SA 3.0), rMD17 (CC0) and the authors' rotation library (see
[`EscheWang/3dcs`](https://huggingface.co/datasets/EscheWang/3dcs)). The rMD17 split files are part of rMD17
(Christensen and von Lilienfeld, 2020).
## Citation
```bibtex
@inproceedings{wang2026threedcs,
title = {3{DCS}: Datasets and Benchmark for Evaluating Conformational Sensitivity in Molecular Representations},
author = {Wang, Xi and Zhang, Yang and Zhang, Yingjia and Cai, Yejia and Wang, Shengjie},
booktitle = {The Fourteenth International Conference on Learning Representations (ICLR)},
year = {2026},
url = {https://openreview.net/forum?id=JAb0y8lkqL}
}
```