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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

from tdc.single_pred import Tox
data = Tox(name = 'herg_karim')

Model description

CNN is applying Convolutional Neural Network on SMILES string fingerprint. Model is tuned with 100 runs using Ax platform.

To load the pre-trained model, type

from tdc import tdc_hf_interface
tdc_hf = tdc_hf_interface("hERG_Karim-CNN")
# 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'])


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