readme: ---
  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
  
  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_herg = tdc_hf_interface("hERG_Karim_Morgan")
  # load deeppurpose model from this repo
  dp_model = tdc_hf_herg.load_deeppurpose('./data')
  dp_model.predict('YOUR SMILES STRING')
  ```
  
  ## 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
  