---
license: unknown
task_categories:
- tabular-classification
- graph-ml
- text-classification
tags:
- chemistry
- biology
- medical
- admet
- molecular-property-prediction
- drug-discovery
- aurigene
pretty_name: MoleculeNet BBBP
size_categories:
- 1K<n<10K
configs:
- config_name: default
  data_files:
  - split: train
    path: bbbp.csv
---

<!-- aurigene-header -->
> ### Mirrored by [Aurigene AI](https://huggingface.co/Aurigene-AI)
> **Discovery stage:** Lead optimization
>
> Blood-brain barrier penetration for small drug-like molecules. Binary classification, scaffold split, AUROC.
>
> **Rows:** 2,039 (bbbp.csv 2,039)
>
> Pairs with [`Aurigene-AI/ChemBERTa-77M-MTR`](https://huggingface.co/Aurigene-AI/ChemBERTa-77M-MTR) from our model catalogue.
>
> Upstream: [`scikit-fingerprints/MoleculeNet_BBBP`](https://huggingface.co/datasets/scikit-fingerprints/MoleculeNet_BBBP) - 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 BBBP

BBBP (Blood-Brain Barrier Penetration) 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 blood-brain barrier penetration (barrier permeability) of small drug-like molecules.

| **Characteristic** | **Description** |
|:------------------:|:---------------:|
|        Tasks       |        1        |
|      Task type     |  classification |
|    Total samples   |       2039      |
|  Recommended split |     scaffold    |
| Recommended metric |      AUROC      |

## References
<a id="1">[1]</a> 
Ines Filipa Martins et al.
"A Bayesian Approach to in Silico Blood-Brain Barrier Penetration Modeling"
J. Chem. Inf. Model. 2012, 52, 6, 1686–1697
https://pubs.acs.org/doi/10.1021/ci300124c

<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