A study on prediction of the bio-toxicity of substituted benzene based on artificial neural network.
Quantitative Structure-Activity Relationship (QSAR) between the bio-toxicity of seventy-eight kinds of substituted benzene chemicals to yeast Saccharomyces cerevisiae (1g(1/Cmiz)) and the components of vertex degree autocorrelation vectors (values of A, B, C and D) was studied by using the software of Artificial Neural Network (ANN). The key factors of the autocorrelation descriptors for the 1g (1/Cmiz) value of yeast Saccharomyces cerevisiae, A [0], A [1], C [3], C [5] and D [3] were selected from twenty-four descriptors, and were explained theoretically in this paper. The QSAR-ANN model has been used to predict the bio-toxicity of twenty-three substituted benzene chemicals. The correlation between Cmiz and LC50 was also discussed, and the liner correlation equation between them was established.
Gao
Da-Wen
DW
School of Municipal and Environmental Engineering, Harbin Institute of Technology, Harbin, China. da-wengao@hotmail.com
Wang
Peng
P
Liang
Hong
H
Peng
Yong-Zhen
YZ
D001554:Benzene / Q000633:toxicity*; D001555:Benzene Derivatives / Q000633:toxicity*; D001681:Biological Assay; D007928:Lethal Dose 50; D016571:Neural Networks, Computer; D011237:Predictive Value of Tests; D021281:Quantitative Structure-Activity Relationship; D012441:Saccharomyces cerevisiae / Q000187:drug effects* / Q000254:growth & development; D012680:Sensitivity and Specificity; D013329:Structure-Activity Relationship
D001555:Benzene Derivatives; D001554:Benzene