| --- |
| 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). |
| |
| |
| ```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 |
| |
| 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 |
| |
| ```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} |
| } |
| ``` |
| |
