Create README.md

README.md

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+---
+language:
+- en
+metrics:
+- accuracy
+- AUC ROC
+- precision
+- recall
+tags:
+- biology
+- chemistry
+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.
+
+## Task description
+Binary classification. Given a drug SMILES string, predict whether it blocks (1, <10uM) or not blocks (0, >=10uM).
+
+## Dataset statistics
+Total: 13445; Train_val: 12620; Test: 825
+
+## 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 Tox
+data = Tox(name = 'herg_karim')
+```
+
+## Model description
+Morgan chemical fingerprint with an MLP decoder. 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("hERG_Karim-Morgan")
+# load deeppurpose model from this repo
+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