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 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 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%|ββββββββββββββββββββββββββββββββββββββ| 100/100 [00:00<00:00, 635500.61%/s] Downloading data: 100%|βββββββββββββββββββββββββββββββββ| 82808/82808 [00:00<00:00, 524655476.79%/s] Downloading data: 100%|βββββββββββββββββββββββββββββββββ| 27480/27480 [00:00<00:00, 195686712.94%/s] Generating test split: 100%|ββββββββββββββββββββββββββ| 795/795 [00:00<00:00, 5218265.54 examples/s] Generating train split: 100%|βββββββββββββββββββββββββ| 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).
###Citation