Accurate In Silico log P Predictions: One Can't Embrace the Unembraceable

被引：28

作者：

Tetko, Igor V. ^{[1
,2
]}

Poda, Gennadiy I. ^{[3
]}

Ostermann, Claude ^{[4
]}

Mannhold, Raimund ^{[5
]}

机构：

[1] German Res Ctr Environm Hlth GmbH, Helmholtz Zentrum Munchen, Inst Bioinformat & Syst Biol, D-85764 Neuherberg, Germany

[2] Inst Bioorgan Chem & Petrochem, UA-02660 Kiev, Ukraine

[3] Pfizer Global R&D, Chesterfield, MO 63017 USA

[4] Nycomed GmbH, D-78467 Constance, Germany

[5] Univ Dusseldorf, Mol Drug Res Grp, D-40225 Dusseldorf, Germany

来源：

QSAR & COMBINATORIAL SCIENCE | 2009年 / 28卷 / 08期

关键词：

Lipophilicity; Structure-property relationships; Computational chemistry; ASSOCIATIVE NEURAL NETWORKS; APPLICABILITY DOMAIN; LIPOPHILICITY; ALOGPS-2.1; CHEMISTRY; QSAR;

D O I：

10.1002/qsar.200960003

中图分类号：

R914 [药物化学];

学科分类号：

100701 ;

摘要：

Prediction accuracy of in silico methods for physicochemical and ADMET properties of drugs is an actual matter of controversial discussions. With a particular concern on log P prediction methods, we discuss here, how understanding the limitations of methods, their applicability domains and their prediction accuracies, as well as the use of local models can help to establish accurate and meaningful in silico predictions.

引用

页码：845 / 849

页数：5

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