Application of ALOGPS 2.1 to predict log D distribution coefficient for Pfizer proprietary compounds

被引：98

作者：

Tetko, IV

Poda, GI

机构：

[1] Ukrainian Acad Sci, Biomed Dept, Inst Bioorgan & Petr Chem, UA-02094 Kiev, Ukraine

[2] Pfizer Global Res & Dev, Struct & Computat Chem, Chesterfield, MO 63017 USA

来源：

JOURNAL OF MEDICINAL CHEMISTRY | 2004年 / 47卷 / 23期

关键词：

D O I：

10.1021/jm049509l

中图分类号：

R914 [药物化学];

学科分类号：

100701 ;

摘要：

Evaluation of the ALOGPS, ACD Labs LogD, and PALLAS PrologD suites to calculate the log D distribution coefficient resulted in high root-mean-squared error (RMSE) of 1.0-1.5 log for two in-house Pfizer's log D data sets of 17 861 and 640 compounds. Inaccuracy in log P prediction was the limiting factor for the overall log D estimation by these algorithms. The self-learning feature of the ALOGPS (LIBRARY mode) remarkably improved the accuracy in log D prediction, and an rmse of 0.64-0.65 was calculated for both data sets.

引用

页码：5601 / 5604

页数：4

共 33 条

[1] In silico prediction of drug-binding strengths to human serum albumin [J].