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--- |
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license: unknown |
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task_categories: |
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- tabular-classification |
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- graph-ml |
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- text-classification |
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tags: |
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- chemistry |
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- biology |
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- medical |
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pretty_name: MoleculeNet BBBP |
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size_categories: |
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- 1K<n<10K |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: "bbbp.csv" |
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--- |
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# MoleculeNet BBBP |
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BBBP (Blood-Brain Barrier Penetration) dataset [[1]](#1), part of MoleculeNet [[2]](#2) benchmark. It is intended to be used through |
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[scikit-fingerprints](https://github.com/scikit-fingerprints/scikit-fingerprints) library. |
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The task is to predict blood-brain barrier penetration (barrier permeability) of small drug-like molecules. |
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| **Characteristic** | **Description** | |
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|:------------------:|:---------------:| |
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| Tasks | 1 | |
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| Task type | classification | |
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| Total samples | 2039 | |
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| Recommended split | scaffold | |
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| Recommended metric | AUROC | |
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## References |
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<a id="1">[1]</a> |
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Ines Filipa Martins et al. |
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"A Bayesian Approach to in Silico Blood-Brain Barrier Penetration Modeling" |
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J. Chem. Inf. Model. 2012, 52, 6, 1686–1697 |
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https://pubs.acs.org/doi/10.1021/ci300124c |
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<a id="2">[2]</a> |
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Wu, Zhenqin, et al. |
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"MoleculeNet: a benchmark for molecular machine learning." |
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Chemical Science 9.2 (2018): 513-530 |
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https://pubs.rsc.org/en/content/articlelanding/2018/sc/c7sc02664a |
