About B3DB

In this repo, we present a large benchmark dataset, Blood-Brain Barrier Database (B3DB), 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 and this repository 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 in this 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.

Citation

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

@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}
}

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.

Usage

There are two types of dataset in B3DB, regression data and classification data and they can be loaded simply using pandas. For example

import pandas as pd

# load regression dataset
regression_data = pd.read_csv("B3DB/B3DB_regression.tsv",
                              sep="\t")

# load classification dataset
classification_data = pd.read_csv("B3DB/B3DB_classification.tsv",
                                  sep="\t")

# load extended regression dataset
regression_data_extended = pd.read_csv("B3DB/B3DB_regression_extended.tsv.gz", 
                                       sep="\t", compression="gzip")

# load extended classification dataset
classification_data_extended = pd.read_csv("B3DB/B3DB_classification_extended.tsv.gz",
                                           sep="\t", compression="gzip")

We also have three examples to show how to use our dataset, numerical_data_analysis.ipynb, PCA_projection_fingerprint.ipynb and PCA_projection_descriptors.ipynb. PCA_projection_descriptors.ipynb uses precomputed chemical descriptors for visualization of chemical space of B3DB, and can be used directly using MyBinder, . Due to the difficulty of installing RDKit in MyBinder, only PCA_projection_descriptors. ipynb is set up in MyBinder.

Working environment setting up

All the calculations were performed in a Python 3.7.9 virtual environment created with Conda in CentOS Linux release 7.9.2009. The Conda environment includes the following Python packages,

• ChEMBL_Structure_Pipeline==1.0.0, https://github.com/chembl/ChEMBL_Structure_Pipeline/
• RDKit==2020.09.1, https://www.rdkit.org/
• openeye-toolkit==2020.2.0, https://docs.eyesopen.com/toolkits/python/index.html/
• mordred==1.1.2, https://github.com/mordred-descriptor/mordred/ (required networkx==2.3.0)
• numpy==1.19.2, https://numpy.org/
• pandas==1.2.1, https://pandas.pydata.org/
• pubchempy==1.0.4, https://github.com/mcs07/PubChemPy/
• PyTDC==0.1.5, https://github.com/mims-harvard/TDC/
• SciPy==1.10.0, https://www.scipy.org/
• tabula-py==2.2.0, https://pypi.org/project/tabula-py/

To creat a virtual environment named bbb_data with Python 3.7.9 to this specification, first,

conda create bbb_py37 python=3.7.9

Given that RDKit, ChEMBL_Structure_Pipeline are not available in PyPI and we will install them with conda,

# activate a virtual environment
conda activate bbb_py37

conda install -c rdkit rdkit=2020.09.1.0
conda install -c conda-forge chembl_structure_pipeline=1.0.0
# https://docs.eyesopen.com/toolkits/python/quickstart-python/linuxosx.html
conda install -c openeye openeye-toolkits=2020.2.0

Then we can install the requirements in requirements.txt with

pip install -r requirements.txt

An easier way is to run the follow script with bash,

#!/bin/bash

# create virtual environment
conda create bbb_py37 python=3.7.9
# activate virtual environment
conda activate bbb_py37

# install required packages
conda install -c rdkit rdkit=2020.09.1.0
conda install -c conda-forge chembl_structure_pipeline=1.0.0
# https://docs.eyesopen.com/toolkits/python/quickstart-python/linuxosx.html
conda install -c openeye openeye-toolkits=2020.2.0

pip install -r requirements.txt

ALOGPS version 2.1 can be accessed at http://www.vcclab.org/lab/alogps/.

The materials and data under this repo are distributed under the CC0 Licence.