| Article ID | Journal | Published Year | Pages | File Type |
|---|---|---|---|---|
| 1367982 | Bioorganic & Medicinal Chemistry Letters | 2005 | 6 Pages |
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.
Graphical abstractComputational QSAR models constructed from pharmacophore-based GRIND descriptors were found to be predictive for hERG channel blockers.Figure optionsDownload full-size imageDownload as PowerPoint slide
Related Topics
Physical Sciences and Engineering
Chemistry
Organic Chemistry
Authors
Giovanni Cianchetta, Yi Li, Jiesheng Kang, David Rampe, Arnaldo Fravolini, Gabriele Cruciani, Roy J. Vaz,