陈艳, 堵锡华, 吴琼, 石春玲. 2-芳基苯并二氢吡喃-4-酮类衍生物抗菌活性的定量构效关系研究及分子设计[J]. 云南大学学报(自然科学版), 2020, 42(2): 332-337. doi: 10.7540/j.ynu.20190601
引用本文: 陈艳, 堵锡华, 吴琼, 石春玲. 2-芳基苯并二氢吡喃-4-酮类衍生物抗菌活性的定量构效关系研究及分子设计[J]. 云南大学学报(自然科学版), 2020, 42(2): 332-337. doi: 10.7540/j.ynu.20190601
CHEN Yan, DU Xi-hua, WU Qiong, SHI Chun-ling. QSAR Study on the antifungal activity of 2-heteroaryl-4-chromanone derivatives and their molecular design[J]. Journal of Yunnan University: Natural Sciences Edition, 2020, 42(2): 332-337. DOI: 10.7540/j.ynu.20190601
Citation: CHEN Yan, DU Xi-hua, WU Qiong, SHI Chun-ling. QSAR Study on the antifungal activity of 2-heteroaryl-4-chromanone derivatives and their molecular design[J]. Journal of Yunnan University: Natural Sciences Edition, 2020, 42(2): 332-337. DOI: 10.7540/j.ynu.20190601

2-芳基苯并二氢吡喃-4-酮类衍生物抗菌活性的定量构效关系研究及分子设计

QSAR Study on the antifungal activity of 2-heteroaryl-4-chromanone derivatives and their molecular design

  • 摘要: 为了研究2-杂环芳基苯并二氢吡喃-4-酮类衍生物对水稻稻疫病杀菌活性,开发新型高活性杀菌剂. 采用分子连接性指数和分子电性距离矢量表征2-杂环芳基苯并二氢吡喃-4-酮类衍生物的分子结构,通过最佳变量子集回归的方法建立34个化合物杀菌活性的四元线性回归方程,非交叉相关系数(R2)和交叉相关系数(R^2_\rmCV) 分别为0.854和0.788,该模型经统计方法验证具有良好的鲁棒性和预测能力. 以模型中的4个变量X1M36M14M32作为人工神经网络的输入层,设定4∶3∶1的神经网络结构构建BP神经网络算法模型,总相关系数达到0.983. 结果表明:2-杂环芳基苯并二氢吡喃-4-酮类衍生物的杀菌活性与4种结构参数呈现良好的非线性关系. 由结构修饰提出了4个具有较高杀菌活性的化合物,有待以后生物实验予以证实.

     

    Abstract: In order to study the antifungal activity of 2-heteroaryl-4-chromanone derivatives to rice blight and develop the novel bactericide with high activity, the molecular structure of 2-heteroaryl-4-chromanone derivatives were characterized by molecular connectivity index and molecular electrical distance vector. The four-parameter(X1, M36, M14, M32) QSAR model of pIC50 was constructed by leaps-and-bounds regression(LBR), the traditional correlation coefficient (R2) and the cross-validation correlation coefficient (R^2_\rmCV) of leave-one-out (LOO) were 0.854 and 0.788, respectively. The model showed predictability and robustness by the verification. The four structural parameters were used as the input neurons of artificial neural network, and a 4∶3∶1 network architecture was employed. The total correlation coefficient was 0.983. The results show that there is good nonlinear relationship between the activity (pIC50) and the four molecular structure parameters. According to the results obtained from the structural modifications, four compounds with high antifungal activity were proposed, and it is expected to be confirmed by using biologic relationships.

     

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