---
license: unknown
task_categories:
- tabular-regression
- graph-ml
- text-classification
tags:
- chemistry
- biology
- medical
- molecular-property-prediction
- drug-discovery
- aurigene
pretty_name: MoleculeNet FreeSolv
size_categories:
- n<1K
configs:
- config_name: default
  data_files:
  - split: train
    path: freesolv.csv
---

<!-- aurigene-header -->
> ### Mirrored by [Aurigene AI](https://huggingface.co/Aurigene-AI)
> **Discovery stage:** Lead optimization
>
> Experimental and calculated hydration free energies in water. Regression.
>
> **Rows:** 642 (freesolv.csv 642)
>
> 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_FreeSolv`](https://huggingface.co/datasets/scikit-fingerprints/MoleculeNet_FreeSolv) - 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 FreeSolv

FreeSolv (Free Solvation Database) 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 hydration free energy of small molecules in water. Targets are in kcal/mol.

| **Characteristic** | **Description** |
|:------------------:|:---------------:|
|        Tasks       |        1        |
|      Task type     |    regression   |
|    Total samples   |       642       |
|  Recommended split |     scaffold    |
| Recommended metric |       RMSE      |

## References
<a id="1">[1]</a> 
Mobley, D.L., Guthrie, J.P.
"FreeSolv: a database of experimental and calculated hydration free energies, with input files"
J Comput Aided Mol Des 28, 711–720 (2014)
https://link.springer.com/article/10.1007/s10822-014-9747-x

<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