Predictive models for hERG potassium channel blockers

Giovanni Cianchetta; Yi Li; Jiesheng Kang; David Rampe; Arnaldo Fravolini; Gabriele Cruciani; Roy J Vaz

doi:10.1016/j.bmcl.2005.03.062

Predictive models for hERG potassium channel blockers

Bioorg Med Chem Lett. 2005 Aug 1;15(15):3637-42. doi: 10.1016/j.bmcl.2005.03.062.

Authors

Giovanni Cianchetta¹, Yi Li, Jiesheng Kang, David Rampe, Arnaldo Fravolini, Gabriele Cruciani, Roy J Vaz

Affiliation

¹ Sanofi-Aventis Pharmaceuticals, 1041 Route 202/206 N, Bridgewater, NJ 08807, USA.

PMID: 15978804
DOI: 10.1016/j.bmcl.2005.03.062

Abstract

We report here a general method for the prediction of hERG potassium channel blockers using computational models generated from correlation analyses of a large dataset and pharmacophore-based GRIND descriptors. These 3D-QSAR models are compared favorably with other traditional and chemometric based HQSAR methods.

MeSH terms

Anti-Arrhythmia Agents / chemistry
Anti-Arrhythmia Agents / pharmacology
Binding Sites
Humans
Inhibitory Concentration 50
Isomerism
Models, Molecular*
Potassium Channel Blockers / chemistry*
Potassium Channel Blockers / pharmacology
Potassium Channels, Voltage-Gated / antagonists & inhibitors*
Quantitative Structure-Activity Relationship
Software*

Substances

Anti-Arrhythmia Agents
Potassium Channel Blockers
Potassium Channels, Voltage-Gated