Prediction of mutagenic toxicity by combination of recursive partitioning and support vector machines

被引：27

作者：

Liao, Quan ^{[1
]}

Yao, Jianhua ^{[1
]}

Yuan, Shengang ^{[1
]}

机构：

[1] Chinese Acad Sci, Shanghai Inst Organ Chem, Dept Comp Chem & Chemoinformat, Shanghai 200032, Peoples R China

来源：

MOLECULAR DIVERSITY | 2007年 / 11卷 / 02期

基金：

中国国家自然科学基金;

关键词：

prediction; mutagenic toxicity; substructural descriptor; recursive partitioning; support vector machines;

D O I：

10.1007/s11030-007-9057-5

中图分类号：

Q5 [生物化学]; Q7 [分子生物学];

学科分类号：

071010 ; 081704 ;

摘要：

The study of prediction of toxicity is very important and necessary because measurement of toxicity is typically time-consuming and expensive. In this paper, Recursive Partitioning (RP) method was used to select descriptors. RP and Support Vector Machines (SVM) were used to construct structure-toxicity relationship models, RP model and SVM model, respectively. The performances of the two models are different. The prediction accuracies of the RP model are 80.2% for mutagenic compounds in MDL's toxicity database, 83.4% for compounds in CMC and 84.9% for agrochemicals in in-house database respectively. Those of SVM model are 81.4%, 87.0% and 87.3% respectively.

引用

页码：59 / 72

页数：14

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