Create README.md

README.md

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+---
+language:
+- en
+metrics:
+- accuracy
+- AUC ROC
+- precision
+- recall
+pipeline_tag: molecule-property-prediction
+tags:
+- biology
+- chemistry
+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 protection 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 development of drugs for central nervous system.
+
+## Task description
+Binary classification. Given a drug SMILES string, predict the activity of BBB.
+
+## Dataset statistics
+Total: 1,975; Train_val: 1,580; Test: 395
+
+## 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
+AttentiveFP is a Graph Attention Network-based molecular representation learning method. Model is tuned with 100 runs using Ax platform.
+To load the pre-trained model, type
+
+```python
+from tdc import tdc_hf_interface
+tdc_hf = tdc_hf_interface("BBB_Martins-AttentiveFP")
+# load deeppurpose model from this repo
+dp_model = tdc_hf_herg.load_deeppurpose('./data')
+tdc_hf.predict_deeppurpose(dp_model, [YOUR SMILES STRING'])
+```
+
+## References:
+[1] 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.