神经网络用于卤代肉桂酸对羊角月牙藻毒性的预测研究
Predictive Study on Toxicity of Halogenated Cinnamic Acid to Selenastrum capricornutum by Neural Network Method
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摘要: 卤代肉桂酸因具有肉桂酸一样的抑菌、抗氧化和抗微生物活性作用，故被广泛应用于医药、化妆品和农药等生产行业。为了研究卤代肉桂酸对羊角月牙藻急性毒性与其分子结构之间的定量构效关系，基于分子拓扑理论，计算了14个卤代肉桂酸分子的4类分子结构参数，筛选了电性拓扑状态指数（E13）和电性距离矢量（M15）作为理论结构描述符，将其与卤代肉桂酸对羊角月牙藻急性毒性进行回归分析，并将这2种参数作为神经网络法的输入层参数，采用2-2-1的神经网络结构，构建了相关性良好的预测毒性的神经网络模型，模型的总相关系数（R总）为0.951，预测的毒性值与其相应文献实验值的相对平均误差为4.49%，吻合度较好，利用模型预测了另外12种卤代肉桂酸对羊角月牙藻急性毒性。结果表明，卤代肉桂酸对羊角月牙藻急性毒性与2种分子结构参数具有良好的非线性关系。Abstract: Halogenated cinnamic acid has the same bacteriostatic, antioxidant and antimicrobial activity as cinnamic acid, so that it is widely used in pharmaceuticals, cosmetics, pesticide and other production industries. In order to study the quantitative structure-activity relationship (QSAR) between the acute toxicity of halogenated cinnamic acid to Selenastrum capricornutum and its molecular structure, four kinds of molecular structure parameters of 14 halogenated cinnamic acid molecules were calculated based on the molecular topological theory. Thus, the electrical topology state index (E13) and the electrical distance vector (M15) were taken as theoretical structure descriptors and introduced to the regression analysis on acute toxicity of halogenated cinnamic acid to Selenastrum capricornutum. Using the two molecular parameters as input layer parameters of the neural network, and 2-2-1 network structure was constructed to predict toxicity with good correlation. The total correlation coefficient (Rt) was 0.951. The average relative error between the experimental values of literature and the predicted values of EC50 was 4.49%. The model was applied to predict the acute toxicity of 12 other kinds of halogenated cinnamic acid to Selenastrum capricornutum. The results showed that there is good non-linear relationship between the toxicity and the two molecular parameters.
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