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Bioinformatics Advance Access originally published online on July 12, 2006
Bioinformatics 2006 22(17):2099-2106; doi:10.1093/bioinformatics/btl352
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© The Author 2006. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Mutagenic probability estimation of chemical compounds by a novel molecular electrophilicity vector and support vector machine

Mingyue Zheng 1, Zhiguo Liu 1, Chunxia Xue 1, Weiliang Zhu 1, Kaixian Chen 1, Xiaomin Luo 1,* and Hualiang Jiang 1,2,*

1 Shanghai Institute of Materia Medica, Shanghai Institutes of Biological Sciences Chinese Academy of Sciences, 555 Zu Chong Zhi Road, Shanghai 201203, China
2 School of Pharmacy, East-China University of Science and Technology Shanghai 200237, China

*To whom correspondence should be addressed.

Motivation: Mutagenicity is among the toxicological end points that pose the highest concern. The accelerated pace of drug discovery has heightened the need for efficient prediction methods. Currently, most available tools fall short of the desired degree of accuracy, and can only provide a binary classification. It is of significance to develop a discriminative and informative model for the mutagenicity prediction.

Results: Here we developed a mutagenic probability prediction model addressing the problem, based on datasets covering a large chemical space. A novel molecular electrophilicity vector (MEV) is first devised to represent the structure profile of chemical compounds. An extended support vector machine (SVM) method is then used to derive the posterior probabilistic estimation of mutagenicity from the MEVs of the training set. The results show that our model gives a better performance than TOPKAT (http://www.accelrys.com) and other previously published methods. In addition, a confidence level related to the prediction can be provided, which may help people make more flexible decisions on chemical ordering or synthesis.

Availability: The binary program (ZGTOX_1.1) based on our model and samples of input datasets on Windows PC are available at http://dddc.ac.cn/adme upon request from the authors.

Contact: hljiang{at}mail.shcnc.ac.cn; xmluo{at}mail.shcnc.ac.cn

Received on April 17, 2006; revised on June 23, 2006; accepted on June 23, 2006

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