Datasets:

TabBioMed
/

QM9_gap

license: cc-by-4.0
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

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).

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

Source: MoleculeNet (https://moleculenet.org) — loaded via DeepChem.

This derived dataset is distributed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Please also cite and respect the terms of the original source.

Citation

@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}
}