
## Dataset description

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).

## Dataset statistics
Total: 13445; Train_val: 12620; Test: 825

## Dataset split
Random split with 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
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
from tdc import tdc_hf_interface
tdc_hf = tdc_hf_interface("hERG_Karim-AttentiveFP")
# 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
* 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
{"language": ["en"], "license": "mit", "library_name": "tdc", "tags": ["biology", "chemistry", "therapeutic science", "drug design", "drug development", "therapeutics"], "metrics": ["accuracy", "AUC ROC", "precision", "recall"]}
tdc biology chemistry therapeutic science drug design drug development therapeutics en license:mit region:us
TAGS
#tdc #biology #chemistry #therapeutic science #drug design #drug development #therapeutics #en #license-mit #region-us 


## Dataset description

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).

## Dataset statistics
Total: 13445; Train_val: 12620; Test: 825

## Dataset split
Random split with 70% training, 10% validation, and 20% testing

To load the dataset in TDC, type



## Model description
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



## References
* Dataset entry in Therapeutics Data Commons, URL 
* 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). URL
## Dataset description

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). ## Dataset statistics
Total: 13445; Train_val: 12620; Test: 825 ## Dataset split
Random split with 70% training, 10% validation, and 20% testing

To load the dataset in TDC, type ## Model description
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 ## References
* Dataset entry in Therapeutics Data Commons, URL 
* 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). URL
TAGS
#tdc #biology #chemistry #therapeutic science #drug design #drug development #therapeutics #en #license-mit #region-us 
 ## Dataset description

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). ## Dataset statistics
Total: 13445; Train_val: 12620; Test: 825 ## Dataset split
Random split with 70% training, 10% validation, and 20% testing

To load the dataset in TDC, type ## Model description
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 ## References
* Dataset entry in Therapeutics Data Commons, URL 
* 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). URL
TAGS
#tdc #biology #chemistry #therapeutic science #drug design #drug development #therapeutics #en #license-mit #region-us 
## Dataset description

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).## Dataset statistics
Total: 13445; Train_val: 12620; Test: 825## Dataset split
Random split with 70% training, 10% validation, and 20% testing

To load the dataset in TDC, type## Model description
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## References
* Dataset entry in Therapeutics Data Commons, URL 
* 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). URL