{"id":20795304,"url":"https://github.com/theochem/b3db","last_synced_at":"2025-05-06T00:53:49.172Z","repository":{"id":38849642,"uuid":"341273108","full_name":"theochem/B3DB","owner":"theochem","description":"A large benchmark dataset, Blood-Brain Barrier Database (B3DB), complied from 50 published resources.","archived":false,"fork":false,"pushed_at":"2023-07-18T17:33:49.000Z","size":166807,"stargazers_count":57,"open_issues_count":5,"forks_count":29,"subscribers_count":7,"default_branch":"main","last_synced_at":"2025-05-06T00:53:39.366Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"cc0-1.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/theochem.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":"CITATION.cff","codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2021-02-22T17:00:14.000Z","updated_at":"2025-05-01T07:53:02.000Z","dependencies_parsed_at":"2022-08-28T03:43:23.518Z","dependency_job_id":"913d129b-b89b-48e3-b8df-5a28086ec617","html_url":"https://github.com/theochem/B3DB","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/theochem%2FB3DB","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/theochem%2FB3DB/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/theochem%2FB3DB/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/theochem%2FB3DB/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/theochem","download_url":"https://codeload.github.com/theochem/B3DB/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":252601682,"owners_count":21774659,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2024-11-17T16:20:57.619Z","updated_at":"2025-05-06T00:53:48.576Z","avatar_url":"https://github.com/theochem.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# About *B3DB*\n\nIn this repo, we present a large benchmark dataset, [Blood-Brain Barrier Database (B3DB)](https://www.nature.com/articles/s41597-021-01069-5), compiled\nfrom 50 published resources (as summarized at\n[raw_data/raw_data_summary.tsv](raw_data/raw_data_summary.tsv)) and categorized based on\nthe consistency between different experimental references/measurements. This dataset was [published in Scientific Data](https://www.nature.com/articles/s41597-021-01069-5) 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).\n\nA subset of the\nmolecules in B3DB has numerical `logBB` values (1058 compounds), while the whole dataset\nhas categorical (BBB+ or BBB-) BBB permeability labels (7807 compounds). Some physicochemical properties\nof the molecules are also provided. \n\n## Citation\n\nPlease use the following citation in any publication using our *B3DB* dataset:\n\n```md\n@article{Meng_A_curated_diverse_2021,\nauthor = {Meng, Fanwang and Xi, Yang and Huang, Jinfeng and Ayers, Paul W.},\ndoi = {10.1038/s41597-021-01069-5},\njournal = {Scientific Data},\nnumber = {289},\ntitle = {A curated diverse molecular database of blood-brain barrier permeability with chemical descriptors},\nvolume = {8},\nyear = {2021},\nurl = {https://www.nature.com/articles/s41597-021-01069-5},\npublisher = {Springer Nature}\n}\n```\n\n## Features of *B3DB*\n\n1. The largest dataset with numerical and categorical values for Blood-Brain Barrier small molecules\n    (to the best of our knowledge, as of February 25, 2021).\n\n2. Inclusion of stereochemistry information with isomeric SMILES with chiral specifications if\n    available. Otherwise, canonical SMILES are used.\n\n3. Characterization of uncertainty of experimental measurements by grouping the collected molecular\n    data records.\n\n4. Extended datasets for numerical and categorical data with precomputed physicochemical properties\n    using [mordred](https://github.com/mordred-descriptor/mordred).\n\n## Usage\n\nThere are two types of dataset in [B3DB](B3DB), [regression data](B3DB/B3DB_regression.tsv)\nand [classification data](B3DB/B3DB_classification.tsv) and they can be loaded simply using *pandas*. For example\n\n```python\nimport pandas as pd\n\n# load regression dataset\nregression_data = pd.read_csv(\"B3DB/B3DB_regression.tsv\",\n                              sep=\"\\t\")\n\n# load classification dataset\nclassification_data = pd.read_csv(\"B3DB/B3DB_classification.tsv\",\n                                  sep=\"\\t\")\n\n# load extended regression dataset\nregression_data_extended = pd.read_csv(\"B3DB/B3DB_regression_extended.tsv.gz\", \n                                       sep=\"\\t\", compression=\"gzip\")\n\n# load extended classification dataset\nclassification_data_extended = pd.read_csv(\"B3DB/B3DB_classification_extended.tsv.gz\",\n                                           sep=\"\\t\", compression=\"gzip\")\n\n```\n\nWe also have three examples to show how to use our dataset, \n[numerical_data_analysis.ipynb](notebooks/numerical_data_analysis.ipynb), \n[PCA_projection_fingerprint.ipynb](notebooks/PCA_projection_fingerprint.ipynb) and \n[PCA_projection_descriptors.ipynb](notebooks/PCA_projection_descriptors.ipynb). \n[PCA_projection_descriptors.ipynb](notebooks/PCA_projection_descriptors.ipynb) uses precomputed \nchemical descriptors for visualization of chemical space of `B3DB`, and can be used directly\nusing *MyBinder*,\n[![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/theochem/B3DB/main?filepath=notebooks%2FPCA_projection_descriptors.ipynb).\nDue to the difficulty of installing `RDKit` in *MyBinder*, only `PCA_projection_descriptors.\nipynb` is set up in *MyBinder*.\n\n## Working environment setting up\n\nAll the calculations were performed in a Python 3.7.9 virtual environment created with Conda in\nCentOS Linux release 7.9.2009. The Conda environment includes the following Python packages,\n\n- ChEMBL_Structure_Pipeline==1.0.0, https://github.com/chembl/ChEMBL_Structure_Pipeline/\n- RDKit==2020.09.1, https://www.rdkit.org/\n- openeye-toolkit==2020.2.0, https://docs.eyesopen.com/toolkits/python/index.html/\n- mordred==1.1.2, https://github.com/mordred-descriptor/mordred/ (required networkx==2.3.0)\n- numpy==1.19.2, https://numpy.org/\n- pandas==1.2.1, https://pandas.pydata.org/\n- pubchempy==1.0.4, https://github.com/mcs07/PubChemPy/\n- PyTDC==0.1.5, https://github.com/mims-harvard/TDC/\n- SciPy==1.10.0, https://www.scipy.org/\n- tabula-py==2.2.0, https://pypi.org/project/tabula-py/\n\nTo creat a virtual environment named *bbb_data* with `Python 3.7.9` to this specification, first,\n```bash\nconda create bbb_py37 python=3.7.9\n```\nGiven that `RDKit`, `ChEMBL_Structure_Pipeline` are not available in PyPI and we will install\nthem with `conda`,\n\n```bash\n# activate a virtual environment\nconda activate bbb_py37\n\nconda install -c rdkit rdkit=2020.09.1.0\nconda install -c conda-forge chembl_structure_pipeline=1.0.0\n# https://docs.eyesopen.com/toolkits/python/quickstart-python/linuxosx.html\nconda install -c openeye openeye-toolkits=2020.2.0\n```\nThen we can install the requirements in [requirements.txt](requirements.txt) with\n```bash\npip install -r requirements.txt\n```\n\nAn easier way is to run the follow script with `bash`,\n\n```bash\n#!/bin/bash\n\n# create virtual environment\nconda create bbb_py37 python=3.7.9\n# activate virtual environment\nconda activate bbb_py37\n\n# install required packages\nconda install -c rdkit rdkit=2020.09.1.0\nconda install -c conda-forge chembl_structure_pipeline=1.0.0\n# https://docs.eyesopen.com/toolkits/python/quickstart-python/linuxosx.html\nconda install -c openeye openeye-toolkits=2020.2.0\n\npip install -r requirements.txt\n```\n\n`ALOGPS` version 2.1 can be accessed at http://www.vcclab.org/lab/alogps/.\n\nThe materials and data under this repo are distributed under the\n[CC0 Licence](http://creativecommons.org/publicdomain/zero/1.0/).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftheochem%2Fb3db","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftheochem%2Fb3db","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftheochem%2Fb3db/lists"}