---
language: en
license: cc0-1.0
source_datasets: curated
task_categories: 
- tabular-classification
- tabular-regression
tags:
- chemistry
- biology
- medical
pretty_name: Blood-Brain Barrier Database (B3DB)
dataset_summary: >-
  Curation of 50 published resources of categorical and numeric measurements of
  Blood-Brain Barrier penetration.
citation: >-
  @article{
    Meng_A_curated_diverse_2021,
    author = {Meng, Fanwang and Xi, Yang and Huang, Jinfeng and Ayers, Paul W.},
    doi = {10.1038/s41597-021-01069-5},
    journal = {Scientific Data},
    number = {289},
    title = {A curated diverse molecular database of blood-brain barrier permeability with chemical descriptors},
    volume = {8},
    year = {2021},
    url = {https://www.nature.com/articles/s41597-021-01069-5},
    publisher = {Springer Nature}
  }
size_categories:
- 1K<n<10K
config_names:
- B3DB_classification
- B3DB_classification_extended
- B3DB_regression
- B3DB_regression_extended
configs:
- config_name: B3DB_classification
  data_files:
  - split: test
    path: B3DB_classification/test.csv
  - split: train
    path: B3DB_classification/train.csv    
- config_name: B3DB_classification_extended
  data_files:
  - split: test
    path: B3DB_classification_extended/test.csv
  - split: train
    path: B3DB_classification_extended/train.csv
- config_name: B3DB_regression
  data_files:
  - split: test
    path: B3DB_regression/test.csv
  - split: train
    path: B3DB_regression/train.csv       
- config_name: B3DB_regression_extended
  data_files:
  - split: test
    path: B3DB_regression_extended/test.csv
  - split: train
    path: B3DB_regression_extended/train.csv      
dataset_info:
- config_name: B3DB_classification
  features:
    - name: "NO."            
      dtype: int64            
    - name: "compound_name"   
      dtype: string  
    - name: "IUPAC_name"       
      dtype: string 
    - name: "SMILES"          
      dtype: string            
    - name: "CID"   
      dtype: float64  
    - name: "logBB"       
      dtype: float64 
    - name: "BBB+/BBB-"            
      dtype:
        class_label:
          names:
            0: "BBB-"
            1: "BBB+"
    - name: "Inchi"   
      dtype: string 
    - name: "threshold"       
      dtype: float64
    - name: "reference"       
      dtype: string
    - name: "group"            
      dtype: string            
    - name: "comments" 
      dtype: string 
    - name: "ClusterNo"       
      dtype: int64
    - name: "MolCount"       
      dtype: int64
  splits:
    - name: train
      num_bytes: 656000
      num_examples: 5856
    - name: test
      num_bytes: 218640
      num_examples: 1951
- config_name: B3DB_classification_extended
  splits:
    - name: train
      num_bytes: 76221824
      num_examples: 5856
    - name: test
      num_bytes: 25394344
      num_examples: 1951
- config_name: B3DB_regression
  features:    
    - name: "NO."
      dtype: int64
    - name: "compound_name"
      dtype: string
    - name: "IUPAC_name"
      dtype: string
    - name: "SMILES"
      dtype: string
    - name: "CID"
      dtype: string
    - name: "logBB"
      dtype: float64
    - name: "Inchi"
      dtype: string
    - name: "reference"
      dtype: string
    - name: "smiles_result"
      dtype: string
    - name: "group"
      dtype: string
    - name: "comments"
      dtype: float64
    - name: "ClusterNo"
      dtype: int64
    - name: "MolCount"
      dtype: int64      
  splits:
    - name: train
      num_bytes: 82808
      num_examples: 795
    - name: test
      num_bytes: 27480
      num_examples: 263
- config_name: B3DB_regression_extended
  splits:
    - name: train
      num_bytes: 10347848
      num_examples: 795
    - name: test
      num_bytes: 3423336
      num_examples: 263
---

# Blood-Brain Barrier Database (B3DB)

The [Blood-Brain Barrier Database (B3DB)](https://www.nature.com/articles/s41597-021-01069-5)
is a large benchmark dataset compiled from 50 published resources
(as summarized at raw_data/raw_data_summary.tsv) and categorized based on the consistency between
different experimental references/measurements. This dataset was
[published in Scientific Data](https://www.nature.com/articles/s41597-021-01069-5) and
a mirror of the [theochem/B3DB](https://github.com/theochem/B3DB) the official Github repo where it is
occasionally uploaded with new experimental data. Scientists who would like
to contribute data should contact the database's maintainers (e.g., by creating a
[new Issue](https://github.com/theochem/B3DB/issues) in the database).

A subset of the molecules in B3DB has numerical `logBB` values (1058 compounds),
while the whole dataset has categorical (`BBB+` or `BBB-`) BBB permeability labels
(7807 compounds). Some physicochemical properties of the molecules are also provided.


## Quickstart Usage

### Load a dataset in python
Each subset can be loaded into python using the Huggingface [datasets](https://huggingface.co/docs/datasets/index) library.
First, from the command line install the `datasets` library

    $ pip install datasets

then, from within python load the datasets library

    >>> import datasets
   
and load one of the `B3DB` datasets, e.g.,

    >>> B3DB_classification = datasets.load_dataset("maomlab/B3DB", name = "B3DB_classification")
    Downloading readme: 100%|████████████████████████| 4.40k/4.40k [00:00<00:00, 1.35MB/s]
    Downloading data: 100%|██████████████████████████| 680k/680k [00:00<00:00, 946kB/s]
    Downloading data: 100%|██████████████████████████| 2.11M/2.11M [00:01<00:00, 1.28MB/s]
    Generating test split: 100%|█████████████████████| 1951/1951 [00:00<00:00, 20854.95 examples/s]
    Generating train split: 100%|████████████████████| 5856/5856 [00:00<00:00, 144260.80 examples/s]

and inspecting the loaded dataset

    >>> B3DB_classification
    B3DB_classification
    DatasetDict({
      test: Dataset({
         features: ['NO.', 'compound_name', 'IUPAC_name', 'SMILES', 'CID', 'logBB', 'BBB+/BBB-', 'Inchi', 'threshold', 'reference', 'group', 'comments', 'ClusterNo', 'MolCount'],
         num_rows: 1951
      })
      train: Dataset({
          features: ['NO.', 'compound_name', 'IUPAC_name', 'SMILES', 'CID', 'logBB', 'BBB+/BBB-', 'Inchi', 'threshold', 'reference', 'group', 'comments', 'ClusterNo', 'MolCount'],
          num_rows: 5856
      })
    })

### Use a dataset to train a model
One way to use the dataset is through the [MolFlux](https://exscientia.github.io/molflux/) package developed by Exscientia.
First, from the command line, install `MolFlux` library with `catboost` and `rdkit` support

    pip install 'molflux[catboost,rdkit]'

then load, featurize, split, fit, and evaluate the a catboost model

    import json
    from datasets import load_dataset
    from molflux.datasets import featurise_dataset
    from molflux.features import load_from_dicts as load_representations_from_dicts
    from molflux.splits import load_from_dict as load_split_from_dict
    from molflux.modelzoo import load_from_dict as load_model_from_dict
    from molflux.metrics import load_suite
    
    split_dataset = load_dataset('maomlab/B3DB', name = 'B3DB_classification')
    
    split_featurised_dataset = featurise_dataset(
      split_dataset,
      column = "SMILES",
      representations = load_representations_from_dicts([{"name": "morgan"}, {"name": "maccs_rdkit"}]))

    model = load_model_from_dict({
        "name": "cat_boost_classifier",
        "config": {
            "x_features": ['SMILES::morgan', 'SMILES::maccs_rdkit'],
            "y_features": ['BBB+/BBB-']}})
    
    model.train(split_featurised_dataset["train"])
    preds = model.predict(split_featurised_dataset["test"])
    
    classification_suite = load_suite("classification")
    
    scores = classification_suite.compute(
        references=split_featurised_dataset["test"]['BBB+/BBB-'],
        predictions=preds["cat_boost_classifier::BBB+/BBB-"])


## About the DB3B

### Features of *B3DB*

1. The largest dataset with numerical and categorical values for Blood-Brain Barrier small molecules
    (to the best of our knowledge, as of February 25, 2021).

2. Inclusion of stereochemistry information with isomeric SMILES with chiral specifications if
    available. Otherwise, canonical SMILES are used.

3. Characterization of uncertainty of experimental measurements by grouping the collected molecular
    data records.

4. Extended datasets for numerical and categorical data with precomputed physicochemical properties
    using [mordred](https://github.com/mordred-descriptor/mordred).


### Data splits
The original B3DB dataset does not define splits, so here we have used the `Realistic Split` method described
in [(Martin et al., 2018)](https://doi.org/10.1021/acs.jcim.7b00166).

### Citation
Please use the following citation in any publication using our *B3DB* dataset:

```md
@article{Meng_A_curated_diverse_2021,
author = {Meng, Fanwang and Xi, Yang and Huang, Jinfeng and Ayers, Paul W.},
doi = {10.1038/s41597-021-01069-5},
journal = {Scientific Data},
number = {289},
title = {A curated diverse molecular database of blood-brain barrier permeability with chemical descriptors},
volume = {8},
year = {2021},
url = {https://www.nature.com/articles/s41597-021-01069-5},
publisher = {Springer Nature}
}
```