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license: unknown
task_categories:
- tabular-regression
- graph-ml
- tabular-classification
tags:
- chemistry
- biology
- medical
- solubility
- molecular-property-prediction
- drug-discovery
- aurigene
pretty_name: MoleculeNet ESOL
size_categories:
- 1K<n<10K
configs:
- config_name: default
data_files:
- split: train
path: esol.csv
---
<!-- aurigene-header -->
> ### Mirrored by [Aurigene AI](https://huggingface.co/Aurigene-AI)
> **Discovery stage:** Lead optimization
>
> Aqueous solubility (log mol/L) for small organic molecules. Regression, the standard solubility benchmark.
>
> **Rows:** 1,128 (esol.csv 1,128)
>
> Pairs with [`Aurigene-AI/MoLFormer-XL-both-10pct`](https://huggingface.co/Aurigene-AI/MoLFormer-XL-both-10pct) from our model catalogue.
>
> Upstream: [`scikit-fingerprints/MoleculeNet_ESOL`](https://huggingface.co/datasets/scikit-fingerprints/MoleculeNet_ESOL) - all credit to the original authors and to the researchers who produced the underlying data; the dataset card and licence below are theirs.
>
> Explore the rest of the catalogue: [Molecule Explorer](https://huggingface.co/spaces/Aurigene-AI/molecule-explorer) - [Protein Target Explorer](https://huggingface.co/spaces/Aurigene-AI/protein-target-explorer) - [Drug Discovery Model Hub](https://huggingface.co/spaces/Aurigene-AI/drug-discovery-model-hub)
---
# MoleculeNet ESOL
ESOL (Estimated SOLubility) dataset [[1]](#1), part of MoleculeNet [[2]](#2) benchmark. It is intended to be used through
[scikit-fingerprints](https://github.com/scikit-fingerprints/scikit-fingerprints) library.
The task is to predict aqueous solubility. Targets are log-transformed, and the unit is log mols per litre (log Mol/L).
| **Characteristic** | **Description** |
|:------------------:|:---------------:|
| Tasks | 1 |
| Task type | regression |
| Total samples | 1128 |
| Recommended split | scaffold |
| Recommended metric | RMSE |
## References
<a id="1">[1]</a>
John S. Delaney
"ESOL: Estimating Aqueous Solubility Directly from Molecular Structure"
J. Chem. Inf. Comput. Sci. 2004, 44, 3, 1000–1005
https://pubs.acs.org/doi/10.1021/ci034243x
<a id="2">[2]</a>
Wu, Zhenqin, et al.
"MoleculeNet: a benchmark for molecular machine learning."
Chemical Science 9.2 (2018): 513-530
https://pubs.rsc.org/en/content/articlelanding/2018/sc/c7sc02664a |