基于B-P网络环境污染物结构毒性关系模式

    A Study on QSAR of Environmental Pollution Based on Back-Propagation Neural Networks

    • 摘要: 应用神经网络反向传播B-P模型,进一步探讨了80种硝基多环芳烃的分子结构和对鼠伤寒沙门氏菌致变活性TA98关系的规律.正确预报率与专家系统方法接近等同(0.97).特别是一些带有激活子结构4,4-二硝基联苯,苯并e芘等化合物的预报值与实验符合程度有了明显改进.这说明神经网络在包容"是"与"非"逻辑编码方面,具有自适应,自组织,自学习的容错能力,不失为良好的环境污染物的构效关系模式.

       

      Abstract: Experimental research shows that the literature data on the direct-acting mutagenicities in the Salmonella Typhimurium strain TA98 of nitrated Polycyclic Aromatic Hydrocarbons(PAHs), together with a descriptor represented in this paper, are approximately accordant with the result of a systematic analysis of the performance of back-propagation neural-network models. Based upon two classes of activities, the correct recognition rate can reach up to 97 persent, which is better than the rate obtained through the use of pattern recognition. These results can lead to general recommendations for a proper evaluation of recognition and prediction criteria for this class of nonlinear structure-activity models.

       

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