EscheWang/3dcs-traj-frames

3DCS Trajectory Frames

The trajectory frames of the 3DCS benchmark (Energy axis of the Geometry–Chirality–Energy framework). It has 999,988 rows (one per frame) for 10 rMD17 molecules, stored in 1 parquet file (345.8 MB). The matching per-frame energies are in EscheWang/3dcs-traj-energies.

This repository is a standalone copy of the traj_frames 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.

Field	Type	Description
mol_type	string	rMD17 molecule: rmd17_aspirin, rmd17_azobenzene, rmd17_benzene, rmd17_ethanol, rmd17_malonaldehyde, rmd17_naphthalene, rmd17_paracetamol, rmd17_salicylic, rmd17_toluene or rmd17_uracil.
frame_idx	int32	0-based index of the structure in the rMD17 file. It runs from 0 to n_frames - 1, and the rows for each molecule form one contiguous block in frame_idx order.
mol_block	string	MolBlock of the frame, with explicit hydrogens (coordinates in Å).

Each molecule has 100,000 frames, except rmd17_azobenzene, which has 99,988 (the same count as in rMD17).

Usage

from datasets import load_dataset

ds = load_dataset("EscheWang/3dcs-traj-frames", split="train")
# the same data, as used in the 3DCS toolkit docs:
ds = load_dataset("EscheWang/3dcs", name="traj_frames", 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 the revised MD17 dataset (rMD17) (Christensen & von Lilienfeld, 2020; https://doi.org/10.6084/m9.figshare.12672038). For rmd17_ethanol and rmd17_aspirin, we checked this data against the official rMD17 .npz files. MolBlock coordinates match rMD17 coords to within 5e-5 Å at frames 0, 1, 12345 and 99999, with atoms in rMD17 nuclear_charges order.

License

The data are released under CC BY-SA 4.0. They are derived from rMD17, which is released under CC0. Please also cite rMD17 (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{christensen2020role,
  title   = {On the role of gradients for machine learning of molecular energies and forces},
  author  = {Christensen, Anders S and von Lilienfeld, O Anatole},
  journal = {Machine Learning: Science and Technology},
  volume  = {1},
  number  = {4},
  pages   = {045018},
  year    = {2020}
}