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中国农学通报

中国农学通报 ›› 2023, Vol. 39 ›› Issue (24): 103-107.doi: 10.11924/j.issn.1000-6850.casb2022-0318

• 生物科学 • 上一篇    下一篇

植物精气中萜类化合物分子结构与沸点模拟

高光芹1(), 茹广欣2, 朱秀红2, 黄家荣2, 申文波1()   

  1. 1 河南农业大学理学院，郑州 450002
    2 河南农业大学林学院，郑州 450002
  • 收稿日期:2022-04-28 修回日期:2022-08-27 出版日期:2023-08-25 发布日期:2023-08-23
  • 通讯作者: 申文波，男，1989年生，河南安阳人，教授，博士，主要从事有机合成方法学研究。通信地址：450002 河南省郑州市金水区文化路95号 河南农业大学理学院，Tel：0371-63558881，E-mail：wenboshen@henau.edu.cn。
  • 作者简介:

    高光芹，女，1974年生，四川名山人，高级实验师，硕士，主要从事有机功能小分子研究。通信地址：450002 河南省郑州市金水区文化路95号 河南农业大学理学院，Tel：0371-63558881，E-mail：。

  • 基金资助:
    国家自然科学基金资助项目“基于炔烃的不对称铜催化的氧化反应研究”(22001059); 河南省科技兴林项目“泡桐种质资源发掘与创新利用”(30602126)

Terpenoids in Plant Essence: Molecular Structure and Boiling Point Simulation

GAO Guangqin1(), RU Guangxin2, ZHU Xiuhong2, HUANG Jiarong2, SHEN Wenbo1()   

  1. 1 College of Science, Henan Agricultural University, Zhengzhou 450002
    2 College of Forestry, Henan Agricultural University, Zhengzhou 450002
  • Received:2022-04-28 Revised:2022-08-27 Online:2023-08-25 Published:2023-08-23

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摘要：

为实现化合物分子结构、分子量与沸点之间复杂关系的精确模拟，以植物精气中36种萜类化合物为研究对象，用拓扑指数法量化分子结构，用人工神经网络构建非线性模型。研究结果表明：结构为2:15:1的人工神经网络模型MSBPT，其拟合准确度为96%，预测准确度为91%；引入分子量作为输入变量，对分子结构与沸点的关系具有加强作用。人工神经网络适应于萜类化合物分子结构与其沸点的复杂非线性关系建模和拟合、且预测准确度高；同时，基团贡献法具有广泛适应范围、拓扑指数法计算结果可靠，建议在林业、农业作进一步的深入研究。

关键词: 人工神经网络, 萜类化合物, 分子结构, 拓扑指数, 沸点

Abstract:

In order to accurately simulate the complex relationship between the molecular structure, molecular weight and boiling point of the compounds, 36 terpenoids in plant essence were studied in this essay. The topological index method was used to quantify the molecular structure, and artificial neural networks were applied to construct nonlinear models. The results show that the fitting accuracy of MSBPT with a 2:15:1 structure is 96%, and the prediction accuracy is 91%. The relationship between molecular structure and boiling point is strengthened by introducing molecular weight as an input variable. The artificial neural network is suitable for modeling the complex nonlinear relationship between the molecular structure of terpenoids and their boiling points, with high fitting and prediction accuracy. At the same time, the group contribution method has a wide application range and the topological index method has reliable data, which should be further studied in the field of forestry and agriculture.

Key words: artificial neural network, terpenoids, molecular structure, topological index, boiling point