Background

Human ether-à-go-go related gene (hERG) is crucial for the coordination of the heart's beating. Thus, if a drug blocks the hERG, it could lead to severe adverse effects. Therefore, reliable prediction of hERG liability in the early stages of drug design is quite important to reduce the risk of cardiotoxicity-related attritions in the later development stages.

Description of readout

Task Description: Binary classification. Given a drug SMILES string, predict whether it blocks (1) or not blocks (0).

Data resource

Reference: [1] ADMET Evaluation in Drug Discovery. 16. Predicting hERG Blockers by Combining Multiple Pharmacophores and Machine Learning Approaches