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基于人工神经网络研究小鼠抗搏击能力 被引量:1

Study on anti-fighting activity of mice by artificial neural network
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摘要 为研究30种苯二氮[艹卓]恶唑衍生物对雄性小鼠抗搏击活性(E)的定量构效关系(quantitative structure-activity relationship,QSAR),按照分子的拓扑环境编程计算了30种化合物的电性距离矢量模(M D,D=1,2,…,91).通过逐步回归方法,建立了E的3参数(M_(10)、M_(16)和M_(59))QSAR模型.R_(cv)^(2)和V IF诊断结果显示,该模型具有良好的稳定性和预测能力.将M_(10)、M_(16)和M_(59)作为人工神经网络的输入层结点,采用3∶5∶1的网络结构,利用BP算法获得BP-E模型,其相关系数的平方R 2和标准偏差S分别为0.984和0.054,表明E与上述3参数具有良好的非线性关系.根据进入模型的3个变量可知,影响苯二氮[艹卓]恶唑衍生物对雄性小鼠抗搏击活性的主要因素是—CH 3、—CH 2—、■、—NH—和—OH(O)等微观基团. In order to study the quantitative structure-activity relationship(QSAR)of the anti-fighting activity E for 30 benzodiazepinooxazole derivatives against male mice,the molecular electronegativity distance vector M D(D=1,2,…,91)of these compounds is calculated according to molecular topological environment.The three-variable(M_(59),M_(16),M_(10))QSAR model of E for the compounds is constructed by stepwise regression method.The result demonstrates that the model is robust and has good prediction ability under R 2 cv,V IF tests.The M_(59),M_(16),M_(10)are used as the input neurons of artificial neural network(ANN),and a 3∶5∶1 network architecture is employed.A satisfying BP-E model is constructed with the back-propagation algorithm,with the correlation coefficient R^(2)and the standard error S being 0.984 and 0.054,respectively,showing that the relationship between E and the three structural parameters has a good nonlinear correlation.According to the three parameters of the model,it is clear that the dominant factors that impact the anti-fighting activity of benzodiazepineoxazole derivatives on male mice are the microscopic fragments:—CH 3,—CH 2—,■,—NH—and—OH(O).
作者 朱利兰 冯长君 ZHU Lilan;FENG Changjun(Department of Physical Education,Guangdong Industry Polytechnic,Guangzhou 510300,Guangdong Province,P.R.China;School of Material and Chemical Engineering,Xuzhou University of Technology,Xuzhou 221018,Jiangsu Province,P.R.China)
出处 《深圳大学学报(理工版)》 EI CAS CSCD 北大核心 2021年第3期295-300,共6页 Journal of Shenzhen University(Science and Engineering)
基金 国家自然科学基金资助项目(21075138) 结构化学国家重点实验室基金资助项目(2016028) 广东轻工业职业技术学院自然科学基金资助项目(KJ2019-032)。
关键词 计算化学 苯二氮[艹卓]恶唑衍生物 雄性小鼠 抗搏击活性 电性距离矢量 人工神经网络 定量构效关系 computational chemistry benzodiazepinooxazole derivative male mice anti-fighting activity molecular electronegativity distance vector artificial neural network(ANN) quantitative structure-activity relationship(QSAR)
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