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).
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
python
1from tdc import tdc_hf_interface
2tdc_hf = tdc_hf_interface("hERG_Karim-CNN")3# load deeppurpose model from this repo4dp_model = tdc_hf.load_deeppurpose('./data')5tdc_hf.predict_deeppurpose(dp_model,['CC(=O)NC1=CC=C(O)C=C1'])
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
