tdc/BBB_Martins-CNN

Dataset description

As a membrane separating circulating blood and brain extracellular fluid, the blood-brain barrier (BBB) is the protective layer that blocks most foreign drugs. Thus the ability of a drug to penetrate the barrier to deliver to the site of action forms a crucial challenge in developing drugs for the central nervous system.

Task description

Binary classification. Given a drug SMILES string, predict the activity of BBB.

Dataset statistics

Total: 1,975 drugs

Pre-requisites

Install the following packages

pip install PyTDC
pip install DeepPurpose
pip install git+https://github.com/bp-kelley/descriptastorus
pip install dgl torch torchvision

You can also reference the colab notebook here

Dataset split

Random split with 70% training, 10% validation, and 20% testing

To load the dataset in TDC, type

from tdc.single_pred import ADME
data = ADME(name = 'BBB_Martins')

Model description

CNN is applying Convolutional Neural Network on SMILES string fingerprint. Model is tuned with 100 runs using Ax platform. To load the pre-trained model, type

from tdc import tdc_hf_interface
tdc_hf = tdc_hf_interface("BBB_Martins-CNN")
# load deeppurpose model from this repo
dp_model = tdc_hf.load_deeppurpose('./data')
tdc_hf.predict_deeppurpose(dp_model, ['YOUR SMILES STRING'])

References

• Dataset entry in Therapeutics Data Commons, https://tdcommons.ai/single_pred_tasks/adme/#bbb-blood-brain-barrier-martins-et-al
• Martins, Ines Filipa, et al. “A Bayesian approach to in silico blood-brain barrier penetration modeling.” Journal of chemical information and modeling 52.6 (2012): 1686-1697.