README.md

---
language:
- en
metrics:
- accuracy
- AUC ROC
- precision
- recall
tags:
- biology
- chemistry
- therapeutic science
- drug design
- drug development
- therapeutics
library_name: tdc
license: bsd-2-clause
---

## 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 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](https://colab.research.google.com/drive/1CL92SOCBS-eYDL99w8tjSNIG_ySXzMrG?usp=sharing)


## Dataset split 
Random split on 70% training, 10% validation, and 20% testing

To load the dataset in TDC, type

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

## Model description
Morgan chemical fingerprint with an MLP decoder. The model is tuned with 100 runs using the Ax platform.

```python
from tdc import tdc_hf_interface
tdc_hf = tdc_hf_interface("BBB_Martins-Morgan")
# 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.