Update readme

README.md

@@ -9,6 +9,10 @@ metrics:
 tags:
 - biology
 - chemistry
+- therapeutic science
+- drug design
+- drug development
+- therapeutics
 library_name: tdc
 license: bsd-2-clause
 ---
@@ -23,16 +27,18 @@ 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: 
+## 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.
+AttentiveFP is a Graph Attention Network-based molecular representation learning method. The model is tuned with 100 runs using the Ax platform.
 To load the pre-trained model, type
 
 ```python
@@ -43,5 +49,6 @@ 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.
+## 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.