HergSPred: Accurate Classification of hERG Blockers/Nonblockers with Machine-Learning Models

• Published in: Journal of chemical information and modeling Vol. 62; no. 8; pp. 1830 - 1839
• Main Authors: Zhang, Xudong, Mao, Jun, Wei, Min, Qi, Yifei, Zhang, John Z. H
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 25.04.2022
• Subjects: Cardiotoxicity; Classification; Drug Discovery; Ether-A-Go-Go Potassium Channels; Fingerprints; Humans; Machine Learning; Machine Learning and Deep Learning; Potassium Channel Blockers - chemistry; Potassium Channel Blockers - pharmacology

The human ether-à-go-go-related gene (hERG) K+ channel plays an important role in cardiac action potentials. The inhibition of the hERG channel may lead to long QT syndrome (LQTS) and even sudden cardiac death. Due to severe hERG-related cardiotoxicity, many drugs have been withdrawn from the marke...