EscheWang/3dcs-chirality

3DCS Chirality Dataset

The chirality dataset of the 3DCS benchmark (Chirality axis of the Geometry–Chirality–Energy framework). It has 14,903 rows (one per stereoisomer of a parent molecule) and 52,391 conformers, stored in 1 parquet file (96.0 MB).

This repository is a standalone copy of the chirality config of the combined dataset EscheWang/3dcs. The parquet files are byte-identical.

• Paper (ICLR 2026): https://openreview.net/forum?id=JAb0y8lkqL
• Code (evaluation toolkit, Python module three_dbench): https://github.com/ComDec/3DCS
• Combined dataset (all four configs): https://huggingface.co/datasets/EscheWang/3dcs

Fields

Structures are stored as MDL MolBlock strings (V2000) with 3D coordinates. The only split, train, holds all rows; it is not a training subset. All counts below were computed from the parquet files.

This config has 3,903 distinct parent molecules (ChEMBL IDs) and 14,903 stereoisomers. The number of stereoisomers per parent is 1 (61 parents), 2 (1,148), 3 (121), 4 (682) or 5 (1,891).

Field	Type	Description
key	string	Stereoisomer identifier <mol_id>::en<en_id>_<stereo>. <stereo> lists stereocenters as A<atom index>:<R or S> (0-based atom index in MolBlock atom order), separated by ;. Example: CHEMBL100259::en3_A5:S;A7:S;A10:R;A12:S. In 7 rows, <stereo> is achiral. The key is unique per row.
mol_id	string	ChEMBL ID of the parent molecule.
en_id	string	Index of the stereoisomer within its parent molecule, from "0" to "4". It matches en<k> in key.
n_conformers	int64	Number of conformers of this stereoisomer (1 to 20). It equals len(mol_blocks).
offset	int64	Position of this row's first conformer in the flattened conformer order (rows in file order, then mol_blocks order). It equals the sum of n_conformers over all preceding rows, so a flat embedding array of shape (52391, d) is sliced as [offset : offset + n_conformers].
mol_blocks	list<string>	One MolBlock per conformer, with explicit hydrogens.

Usage

from datasets import load_dataset

ds = load_dataset("EscheWang/3dcs-chirality", split="train")
# the same data, as used in the 3DCS toolkit docs:
ds = load_dataset("EscheWang/3dcs", name="chirality", split="train")

Rebuild RDKit molecules with three_dbench.datasets.serialization.mol_from_block(block) from the 3DCS toolkit. The equivalent RDKit call is Chem.MolFromMolBlock(block, removeHs=False, sanitize=False).

Provenance

• Derived from ChEMBL (Gaulton et al., 2012), using drug-like molecules with annotated stereocenters. Their stereoisomers were enumerated with RDKit, embedded with ETKDG and geometry-optimized once with xtb --opt lax, giving 15,218 stereoisomers with one optimized geometry each.
• The 52,391 conformers released here were derived from those geometries: hydrogens were re-added at RDKit's idealized geometry, rotatable bonds were rotated by a small random torsion (median about 10-12 degrees per bond), and isotropic Gaussian noise of sigma about 0.09 A was added to every atom.
• A conformer was kept only if its heavy-atom RMSD to every already-accepted conformer of the same stereoisomer exceeded 0.350 A, up to a maximum of 20 per stereoisomer. Stereoisomers with no rotatable bond therefore carry exactly one conformer, as do 34% of the 14,903 stereoisomers overall.
• Every released conformer reproduces the CIP (R/S) assignment of its source geometry, as re-perceived from 3D with RDKit.

License

The data are released under CC BY-SA 4.0. They are derived from ChEMBL, which is licensed under CC BY-SA 3.0. Please also cite ChEMBL (see below).

Baseline embeddings

The embedding files of the baseline models evaluated on these data are published at EscheWang/3dcs-embeddings, together with the per-molecule metric outputs of the original evaluation runs and a manifest.csv listing the key, shape, dtype and SHA-256 of every file. The evaluation toolkit is at https://github.com/ComDec/3DCS. The per-model scripts that extract the chirality baseline embeddings, each with its pinned environment and the SHA-256 of the weights it loads, are in the baselines/ directory of that repository.

Citation

@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}
}

Upstream data source:

@article{gaulton2012chembl,
  title   = {ChEMBL: a large-scale bioactivity database for drug discovery},
  author  = {Gaulton, Anna and Bellis, Louisa J and Bento, A Patricia and Chambers, Jon and Davies, Mark and Hersey, Anne and Light, Yvonne and McGlinchey, Shaun and Michalovich, David and Al-Lazikani, Bissan and others},
  journal = {Nucleic Acids Research},
  volume  = {40},
  number  = {D1},
  pages   = {D1100--D1107},
  year    = {2012}
}