Datasets:
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license: cc-by-4.0
pretty_name: ESOL
task_categories:
- tabular-regression
tags:
- tabular
- biomedical
- benchmark
- TabBioMed
- drug-discovery
- regression
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
---
# ESOL
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.
Water solubility data for common organic small molecules. Target: normalized log solubility (measured log solubility in mols per litre, z-scored with the training-split mean and SD, stored as target_mean / target_std). Features: Morgan fingerprints (radius=2). Split: balanced scaffold split 70/15/15 (chemprop scaffold_balanced, seed 42: scaffold sets larger than half the validation or test size are placed first, the remaining sets follow in random order, filling train, then validation, then test).
| Domain | Task | Samples | Features | Classes |
|---|---|---|---|---|
| Drug Discovery | regression | 1,128 | 1,563 | - |
**Splits.** Fixed train/val/test split (789/169/170 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/ESOL")
```
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}
}
```
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