--- 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>> 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: ['B3DB_classification_index', 'compound_name', 'IUPAC_name', 'SMILES', 'CID', 'logBB', 'Y', 'Inchi', 'threshold', 'reference', 'group', 'comments', 'ClusterNo', 'MolCount'], num_rows: 1951 }) train: Dataset({ features: ['B3DB_classification_index', 'compound_name', 'IUPAC_name', 'SMILES', 'CID', 'logBB', 'Y', '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": ['Y']}}) 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"]['Y'], predictions=preds["cat_boost_classifier::Y"]) ## 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} } ```