TabBioMed camera-ready release

README.md

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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}
+}
+```

data/test.parquet

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+size 798774

data/train.parquet

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+size 834521

data/val.parquet

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+oid sha256:01971ceadccc0ac7d2fe169ffd2d3909c378d418784e289b52531ec7eca8396e
+size 799961

metadata.json

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+{
+  "READY": true,
+  "name": "ESOL",
+  "task": "regression",
+  "cat_idx": [],
+  "num_samples": 1128,
+  "num_features": 1563,
+  "description": "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).",
+  "source": "MoleculeNet (https://moleculenet.org) \u2014 loaded via DeepChem.",
+  "label": "Drug Discovery",
+  "sub_labels": [
+    "Physical Chemistry",
+    "Fingerprint"
+  ]
+}