Predictive models for hERG potassium channel blockers

被引：82

作者：

Cianchetta, G

Li, Y

Kang, JS

Rampe, D

Fravolini, A

Cruciani, G

Vaz, RJ

机构：

[1] Sanofi Aventis Pharmaceut, Bridgewater, NJ 08807 USA

[2] Univ Perugia, Dipartimento Chim & Tecnol Farm, I-06123 Perugia, Italy

[3] Univ Perugia, Dipartimento Chim, Lab Chemiometria, I-06123 Perugia, Italy

[4] Mol Discovery Ltd, London W1A 3BQ, England

来源：

BIOORGANIC & MEDICINAL CHEMISTRY LETTERS | 2005年 / 15卷 / 15期

关键词：

D O I：

10.1016/j.bmcl.2005.03.062

中图分类号：

R914 [药物化学];

学科分类号：

100701 ;

摘要：

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. (c) 2005 Elsevier Ltd. All rights reserved.

引用

收藏

页码：3637 / 3642

页数：6

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