Datasets:

TabBioMed
/

ESOL

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).

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

@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}
}