Update readme

README.md

@@ -9,13 +9,17 @@ metrics:
 tags:
 - biology
 - chemistry
+- therapeutic science
+- drug design
+- drug development
+- therapeutics
 library_name: tdc
 license: bsd-2-clause
 ---
 
 ## Dataset description
 
-An integrated Ether-a-go-go-related gene (hERG) dataset consisting of molecular structures labelled as hERG (<10uM) and non-hERG (>=10uM) blockers in the form of SMILES strings was obtained from the DeepHIT, the BindingDB database, ChEMBL bioactivity database, and other literature.
+An integrated Ether-a-go-go-related gene (hERG) dataset consisting of molecular structures labeled as hERG (<10uM) and non-hERG (>=10uM) blockers in the form of SMILES strings was obtained from the DeepHIT, the BindingDB database, ChEMBL bioactivity database, and other literature.
 
 ## Task description
 Binary classification. Given a drug SMILES string, predict whether it blocks (1, <10uM) or not blocks (0, >=10uM).
@@ -23,8 +27,8 @@ Binary classification. Given a drug SMILES string, predict whether it blocks (1,
 ## Dataset statistics
 Total: 13445; Train_val: 12620; Test: 825
 
-## Dataset split: 
-Random split on 70% training, 10% validation, and 20% testing
+## Dataset split
+Random split with 70% training, 10% validation, and 20% testing
 
 To load the dataset in TDC, type
 
@@ -46,5 +50,6 @@ dp_model = tdc_hf.load_deeppurpose('./data')
 tdc_hf.predict_deeppurpose(dp_model, ['CC(=O)NC1=CC=C(O)C=C1'])
 ```
 
-## References:
-[1] Karim, A., et al. CardioTox net: a robust predictor for hERG channel blockade based on deep learning meta-feature ensembles. J Cheminform 13, 60 (2021). https://doi.org/10.1186/s13321-021-00541-z
+## References
+* Dataset entry in Therapeutics Data Commons, https://tdcommons.ai/single_pred_tasks/tox
+* Karim, A., et al. CardioTox net: a robust predictor for hERG channel blockade based on deep learning meta-feature ensembles. J Cheminform 13, 60 (2021). https://doi.org/10.1186/s13321-021-00541-z