[1]朱利兰,冯长君.基于人工神经网络研究小鼠抗搏击能力[J].深圳大学学报理工版,2021,38(3):295-300.[doi:10.3724/SP.J.1249.2021.03295]
 ZHU Lilan and FENG Changjun.Study on anti-fighting activity of mice by artificial neural network[J].Journal of Shenzhen University Science and Engineering,2021,38(3):295-300.[doi:10.3724/SP.J.1249.2021.03295]
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基于人工神经网络研究小鼠抗搏击能力()
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《深圳大学学报理工版》[ISSN:1000-2618/CN:44-1401/N]

卷:
第38卷
期数:
2021年第3期
页码:
295-300
栏目:
生物工程
出版日期:
2021-05-14

文章信息/Info

Title:
Study on anti-fighting activity of mice by artificial neural network
文章编号:
202103012
作者:
朱利兰1冯长君2
1)广东轻工职业技术学院体育部,广东广州 510300
2)徐州工程学院材料与化学工程学院,江苏徐州 221018
Author(s):
ZHU Lilan1 and FENG Changjun2
1) Department of Physical Education, Guangdong Industry Polytechnic, Guangzhou 510300, Guangdong Province, P.R.China
2) School of Material and Chemical Engineering, Xuzhou University of Technology, Xuzhou 221018, Jiangsu Province, P.R.China
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
Keywords:
computational chemistry benzodiazepinooxazole derivative male mice anti-fighting activity molecular electronegativity distance vector artificial neural network (ANN) quantitative structure-activity relationship (QSAR)
分类号:
G804; O6-051
DOI:
10.3724/SP.J.1249.2021.03295
文献标志码:
A
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(MD, D=1, 2, …, 91). 通过逐步回归方法,建立了E的3参数(M10、 M16和M59)QSAR模型. R2cv和VIF诊断结果显示,该模型具有良好的稳定性和预测能力. 将M10、 M16和M59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R2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—CH3、—CH2—、>C<、—NH—和—OH(O)等微观基团.
Abstract:
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 MD (D=1, 2, …, 91) of these compounds is calculated according to molecular topological environment. The three-variable (M59, M16, M10) 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 R2cv, VIF tests. The M59, M16, M10 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 R2 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: —CH3, —CH2—, >C<, —NH— and —OH (O).

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备注/Memo

备注/Memo:
Received:2020-12-04;Accepted:2021-03-05
Foundation:National Natural Science Foundation of China (21075138) ; Special Fund of State Key Laboratory of Structure Chemistry (20160028); Natural Science Foundation of Guangdong Industry Polytechnic (KJ2019-032)
Corresponding author:Professor FENG Changjun. E-mail: fengcj@xzit.edu.cn
Citation:ZHU Lilan, FENG Changjun. Study on anti-fighting activity of mice by artificial neural network[J]. Journal of Shenzhen University Science and Engineering, 2021, 38(3): 295-300.(in Chinese)
基金项目: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)
作者简介:朱利兰(1981—),广东轻工职业技术学院讲师. 研究方向:体育运动学. E-mail: zhulilan_8983@sina.com
引文:朱利兰,冯长君. 基于人工神经网络研究小鼠抗搏击能力[J]. 深圳大学学报理工版,2021,38(3):295-300.
更新日期/Last Update: 2021-05-30