Data Set Modelability by QSAR

被引：107

作者：

Golbraikh, Alexander ^{[1
]}

Muratov, Eugene ^{[1
,2
]}

Fourches, Denis ^{[1
]}

Tropsha, Alexander ^{[1
]}

机构：

[1] Univ N Carolina, Lab Mol Modeling, Div Chem Biol & Med Chem, UNC Eshelman Sch Pharm, Chapel Hill, NC 27599 USA

[2] AV Bogatsky Phys Chem Inst NAS Ukraine, Dept Mol Struct & Cheminformat, UA-65080 Odessa, Ukraine

来源：

JOURNAL OF CHEMICAL INFORMATION AND MODELING | 2014年 / 54卷 / 01期

关键词：

ACTIVITY CLIFFS;

D O I：

10.1021/ci400572x

中图分类号：

R914 [药物化学];

学科分类号：

100701 ;

摘要：

We introduce a simple MODelability Index (MODI) that estimates the feasibility of obtaining predictive QSAR models (correct classification rate above 0.7) for a binary data set of bioactive compounds. MODI is defined as an activity class-weighted ratio of the number of nearest-neighbor pairs of compounds with the same activity class versus the total number of pairs. The MODI values were calculated for more than 100 data sets, and the threshold of 0.65 was found to separate the nonmodelable and modelable data sets.

引用

页码：1 / 4

页数：4

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