文章摘要
引用本文:陈 艳,张昌民,瞿翠玲.苯砜基羧酸酯类化合物急性毒性的预测模型[J].福州大学学报(自然科学版),2016,44(6):891~895
苯砜基羧酸酯类化合物急性毒性的预测模型
Prediction model of the acute toxicity of phenylsulfonyl carboxylate compound
  
DOI:10.7631/issn.1000-2243.2016.06.0891
中文关键词: 苯砜基羧酸酯  急性毒性  分子电性距离矢量  人工神经网络  定量结构-活性相关
英文关键词: phenylsulfonyl carboxylates  acute toxicity  molecular electrongativity distance vector  artificial neural network  QSAR
基金项目:
作者单位
陈 艳 徐州工程学院化学化工学院江苏 徐州 221111 
张昌民 江苏蓝丰生物化工股份有限公司江苏 新沂 221400 
瞿翠玲 徐州工程学院化学化工学院江苏 徐州 221111 
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中文摘要:
      用MATLAB软件编程计算了56个苯砜基羧酸酯类化合物分子电性距离矢量(mk),同时用Hyper chem8.0程序包计算了它们的理化参数. 这两类结构参数被用于建立苯砜基羧酸酯类化合物急性毒性的预测模型. 通过最佳变量子集回归的方法构建多元线性回归模型:-lg EC50=4.724+30.275m7+0.061m24+6.468m85+0.880m90-0.003V-0.096(lg P )2. 该模型具有良好的稳健性和较强的预测能力. 以模型中的6个参数为人工神经网络(ANN)输入层,设定6∶4∶1的网络结构,用BP算法构建人工神经网络模型,其相关系数R2为0.986. 结果表明,神经网络BP算法模型的预测结果优于多元线性回归模型的预测结果.
英文摘要:
      The molecular electrongativity distance vector (mk)and physicochemical parameters of 56 phenylsulfonyl carboxylates were calculated by the software of MATLAB and Hyper chem8.0 for establishing the prediction model of the acute toxicity (-lg EC50 ) of these compounds. The multiple liner regression(MLR)model was constructed by leaps-and-bounds regression: -lg EC50=4.724+30.275m7+0.061m24+6.468m85+0.880m90-0.003V-0.096(lg P )2. The model is highly reliable and has good predictive ability. The six structural parameters were used as the input neurons of artificial neural network,and a 6∶4∶1 network architecture was employed. A satisfied model was constructed with the back-propagation algorithm,the correlation coefficient(R2 ) was 0.986. It can be concluded that the prediction results of BP-ANN model are better than MLR-QSAR model.
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