QSAR Study for Carcinogenic Potency of Aromatic Amines Based on GEP and MLPs

Fucheng Song, Anling Zhang, Hui Liang, Lianhua Cui, Wenlian Li, Hongzong Si, Yunbo Duan and Honglin Zhai
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Fucheng Song: Department of Public Health, Qingdao University Medical College, Qingdao 266071, China
Anling Zhang: Modern Educational Technology Center, Qingdao University, Qingdao 266071, China
Hui Liang: Department of Public Health, Qingdao University Medical College, Qingdao 266071, China
Lianhua Cui: Department of Public Health, Qingdao University Medical College, Qingdao 266071, China
Wenlian Li: Department of Public Health, Qingdao University Medical College, Qingdao 266071, China
Hongzong Si: Institute for Computational Science and Engineering, Laboratory of New Fibrous Materials and Modern Textile, The Growing Base for State Key Laboratory, Qingdao University, Ningxia Road 308, Qingdao 266071, China
Yunbo Duan: Institute for Computational Science and Engineering, Laboratory of New Fibrous Materials and Modern Textile, The Growing Base for State Key Laboratory, Qingdao University, Ningxia Road 308, Qingdao 266071, China
Honglin Zhai: Department of Chemistry, Lanzhou University, Lanzhou 730000, China

IJERPH, 2016, vol. 13, issue 11, 1-14

Abstract: A new analysis strategy was used to classify the carcinogenicity of aromatic amines. The physical-chemical parameters are closely related to the carcinogenicity of compounds. Quantitative structure activity relationship (QSAR) is a method of predicting the carcinogenicity of aromatic amine, which can reveal the relationship between carcinogenicity and physical-chemical parameters. This study accessed gene expression programming by APS software, the multilayer perceptrons by Weka software to predict the carcinogenicity of aromatic amines, respectively. All these methods relied on molecular descriptors calculated by CODESSA software and eight molecular descriptors were selected to build function equations. As a remarkable result, the accuracy of gene expression programming in training and test sets are 0.92 and 0.82, the accuracy of multilayer perceptrons in training and test sets are 0.84 and 0.74 respectively. The precision of the gene expression programming is obviously superior to multilayer perceptrons both in training set and test set. The QSAR application in the identification of carcinogenic compounds is a high efficiency method.

Keywords: QSAR; aromatic amines; gene expression programming; multilayer perceptrons (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2016
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