---
license: mit
task_categories:
- text-classification
language:
- en
tags:
- chemistry
size_categories:
- 1K<n<10K
pretty_name: Blood-Brain Barrier Database
dataset_summary: >- 
 Curation of 50 published resources of categorical and numeric measurements of Blood-Brain Barrier penetration.
citation: >- 
 COPY AND PASTE WHAT YOU GOT FROM THE BIBTEX WEBSITE 

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_regression_extended
  features:
    - name: "NO."
      dtype: int64
    - name: "compound_name"
      dtype: object
    - name: "IUPAC_name"
      dtype: object
    - name: "SMILES"
      dtype: object
    - name: "CID"
      dtype: object
    - name: "logBB"
      dtype: float64
    - name: "Inchi"
      dtype: object
    - name: "reference"
      dtype: object
    - name: "smiles_result"
      dtype: object
    - name: "group"
      dtype: object
    - 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
---

# Blood-Brain Barrier Database


## 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., 


 and inspecting the loaded dataset                          


### 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-"])

    >>> B3DB_classification = datasets.load_dataset("maomlab/B3DB", name = "B3DB_classification")
Downloading readme: 100%|██████████████████████████████████████| 100/100 [00:00<00:00, 635500.61%/s]
Downloading data: 100%|█████████████████████████████████| 82808/82808 [00:00<00:00, 524655476.79%/s]
Downloading data: 100%|█████████████████████████████████| 27480/27480 [00:00<00:00, 195686712.94%/s]
Generating test split: 100%|██████████████████████████| 795/795 [00:00<00:00, 5218265.54 examples/s]
Generating train split: 100%|█████████████████████████| 263/263 [00:00<00:00, 1835444.18 examples/s]## About the B3DB

### Features of B3DB      


### 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