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---
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 |