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README.md

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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