| --- |
| pretty_name: QSAR_biodegradation |
| task_categories: |
| - tabular-classification |
| tags: |
| - tabular |
| - biomedical |
| - benchmark |
| - TabBioMed |
| - drug-discovery |
| - binary |
| size_categories: |
| - 1K<n<10K |
| 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: cc-by-4.0 |
| --- |
| |
| # QSAR_biodegradation |
| |
| 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. |
| |
| Environmental ready-biodegradability classification from 41 molecular descriptors. The TALENT release contains 1,054 rows (UCI lists 1,055); collapsing three redundant descriptor rows with agreeing labels leaves 1,051 rows. Retained values are unchanged. A stratified 70/15/15 split is generated after deduplication. Molecular structures/SMILES are absent from this release, so this is a random-split descriptor benchmark, not a scaffold-generalization test. Grouped with molecular-property tasks under the benchmark drug-discovery domain. |
| |
| | Domain | Task | Samples | Features | Classes | |
| |---|---|---|---|---| |
| | Drug Discovery | binary | 1,051 | 41 | 2 | |
| |
| **Splits.** Fixed train/val/test split (735/158/158 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/QSAR_biodegradation") |
| ``` |
| |
| A Croissant (1.1) description with Responsible-AI fields is distributed with the |
| TabBioMed release. |
| |
| ## Source and license |
| |
| Dataset license: [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/). |
| |
| - [QSAR biodegradation](https://archive.ics.uci.edu/dataset/254/qsar+biodegradation). Kamel Mansouri Tine Ringsted Davide Ballabio Roberto Todeschini Viviana Consonni. UCI Machine Learning Repository. DOI: 10.24432/C5H60M. Source license: [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/). Retain attribution and the license link, identify modifications, and do not imply endorsement. |
| |
| 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} |
| } |
| ``` |
| |
