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Prediction of mutagenic toxicity by combination of Recursive Partitioning and Support Vector Machines

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Abstract

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.

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Abbreviations

SVM:

Support Vector Machines

RP:

Recursive Partitioning

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Acknowledgments

The authors thank Dr. R. Bursi, Dr. S. S. Young and Dr. C. Helma for supplying the data sets. This work was supported in part by the National Basic Research Program (also called 973 Program) of China, through Grants 2003CB114400; by the National High-Tech. Program (also called 863 Program), through Grants 2006AA02Z39; by National Natural Science Foundation of China through Grants 20473112 and 20572120; by Chinese Academy of Sciences, through Grants KGCX2-SW-213-05 and KGCX2-SW-213-01.

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  1. Department of Computer Chemistry and Chemoinformatics, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, 354, Fenglin Road, Shanghai, 200032, China

    Quan Liao, Jianhua Yao & Shengang Yuan

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  1. Quan Liao
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  2. Jianhua Yao
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  3. Shengang Yuan
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Correspondence to Jianhua Yao.

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Liao, Q., Yao, J. & Yuan, S. Prediction of mutagenic toxicity by combination of Recursive Partitioning and Support Vector Machines. Mol Divers 11, 59–72 (2007). https://doi.org/10.1007/s11030-007-9057-5

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