Datasets:
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pretty_name: QM9_gap
task_categories:
- tabular-regression
tags:
- tabular
- biomedical
- benchmark
- TabBioMed
- drug-discovery
- regression
size_categories:
- 100K<n<1M
configs:
- config_name: default
data_files:
- split: train
path: data/train.parquet
- split: validation
path: data/val.parquet
- split: test
path: data/test.parquet
license: cc0-1.0
---
# QM9_gap
Part of **TabBioMed** (*TabBioMed: A Large-Scale Benchmark for Biomedical Tabular Learning*), a benchmark of 94 curated biomedical
tabular datasets. Code, preprocessing and documentation: https://github.com/AI-sandbox/TabBioMed.
Geometric, energetic, electronic and thermodynamic properties of DFT-modelled small molecules. Task: predict 'gap' using Coulomb matrix features. Target: z-scored with the training-split mean and SD (stored as target_mean / target_std), following DeepChem's default target normalization for this loader. Split: random 70/15/15 (seed 42).
| Domain | Task | Samples | Features | Classes |
|---|---|---|---|---|
| Drug Discovery | regression | 131,970 | 841 | - |
**Splits.** Fixed train/val/test split (92380/19795/19795 rows).
## Files
- `data/**.parquet`: one row per sample. `row_idx` is the row index in the original
TabBioMed array; `f_<i>[_<name>]` are the features (in order); `target` (or `target_<j>`
for multi-target tasks) is the label. Categorical feature indices (0-based, among the
features): `[]`. Missing values are stored as nulls.
- `metadata.json`: TabBioMed task metadata (task type, categorical indices, source, description).
```python
from datasets import load_dataset
ds = load_dataset("TabBioMed/QM9_gap")
```
A Croissant (1.1) description with Responsible-AI fields is distributed with the
TabBioMed release.
## Source and license
Dataset license: [CC0-1.0](https://creativecommons.org/publicdomain/zero/1.0/).
- [QM9: Data for 133885 GDB-9 molecules](https://doi.org/10.6084/m9.figshare.1057646). Ramakrishnan, Dral, Rupp and von Lilienfeld (2014), DOI 10.1038/sdata.2014.22. GDB-17: Ruddigkeit et al., DOI 10.1021/ci300415d. MoleculeNet: Wu et al., DOI 10.1039/C7SC02664A. Source license: [CC0-1.0](https://creativecommons.org/publicdomain/zero/1.0/). CC0 applies to the dataset. Retain source citations and identify the selected property and TabBioMed featurization.
See `NOTICE.md` for attribution and modifications and `LICENSES/` for license texts.
## Citation
```bibtex
@inproceedings{tabbiomed2026,
title = {TabBioMed: A Large-Scale Benchmark for Biomedical Tabular Learning},
author = {Mateo Bernad{\'o}, Pau and Polapragada, Saivenkata Nagavyjayanthi and Bonet, David and Arbiol Rakuljic, Pol and Mogas Pladevall, Laia and Comajoan Cara, Mar{\c{c}}al and Gonzalez Ferrer, Jesus and Abante, Jordi and Mas Montserrat, Daniel and Haussler, David and Ioannidis, Alexander G.},
booktitle = {Advances in Neural Information Processing Systems (NeurIPS), Evaluations and Datasets Track},
year = {2026}
}
```
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