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Bioinformatics Advance Access published online on July 12, 2006
Bioinformatics, 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
Received April 17, 2006
Revised June 23, 2006
Accepted June 23, 2006

Article

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

* To whom correspondence should be addressed.
Hualiang Jiang, E-mail: hljiang{at}mail.shcnc.ac.cn


  Abstract

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 data sets on Windows PC are available at http://dddc.ac.cn/adme upon request from the authors.

Associate Editor: Dmitrij Frishman
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