title: Mutagenic probability estimation of chemical compounds by a novel molecular electrophilicity vector and support vector machine.
abstract: BACKGROUND
  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.
  
  
  BACKGROUND
  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.
authors.affiliation: Shanghai Institute of Materia Medica, Shanghai Institutes of Biological Sciences Chinese Academy of Sciences, 555 Zu Chong Zhi Road, Shanghai 201203, China.
authors.forename: Mingyue
authors.initials: M
authors.lastname: Zheng
authors.forename: Zhiguo
authors.initials: Z
authors.lastname: Liu
authors.forename: Chunxia
authors.initials: C
authors.lastname: Xue
authors.forename: Weiliang
authors.initials: W
authors.lastname: Zhu
authors.forename: Kaixian
authors.initials: K
authors.lastname: Chen
authors.forename: Xiaomin
authors.initials: X
authors.lastname: Luo
authors.forename: Hualiang
authors.initials: H
authors.lastname: Jiang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D057927:Hydrophobic and Hydrophilic Interactions; D008954:Models, Biological; D008956:Models, Chemical; D015233:Models, Statistical; D009152:Mutagenicity Tests / Q000379:methods*; D009153:Mutagens / Q000032:analysis* / Q000737:chemistry*; D010363:Pattern Recognition, Automated / Q000379:methods; D021281:Quantitative Structure-Activity Relationship; D012984:Software
chemical_list: D009153:Mutagens