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基于概率神经网络的中草药活性组分利尿性的QSAR研究(英文) 被引量：1

Probabilistic neural network-based on QSAR for the prediction of the diuretic activity of the active constituents of traditional Chinese medicinal herbs
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摘要 利用线性判别分析和概率神经网络,建立了预测中草药有效成分利尿性与其分子结构参数之间的 QSAR 模型．概率神经网络分类结果好,训练集、交互检验集和测试集的分类正确率均可达到100％．本文所用的概率神经网络结构简单、易于调试,研究工作进一步明确了分子利尿性与其结构参数之间的关系,有助于利尿药物的选择与合成． A quantitative structure-activity reIationship（QSAR） method is used for the first time to develop the correlation models between the diuretic activity of the active constituents of traditional Chinese medicinal herbs and a set of three molecular descriptors. Molecular descriptors derived solely from structure were used to represent molecular structures. A subset of the calculated descriptors selected using correlation coefficient matrix and forward regression was used in the QSAR model development, Linear discriminant analysis and probabilistic neural network（PNN） were utilized to construct the linear and nonlinear QSAR model, respectively. The optimal QSAR model developed was based on a PNN with the smoothing parameter σ = 0.75. Fractions correct representing the fraction of cases classified correctly of training, cross validation and test data were all 100%, respectively. It proves that this PNN is a perfect classifier network.
作者 吕文娟 薛春霞 陈兴国 胡之德 刘满仓 范波涛
机构地区 兰州大学化学化工学院 巴黎第七大学ITODYS研究所
出处 《兰州大学学报（自然科学版）》 CAS CSCD 北大核心 2006年第2期72-76,共5页 Journal of Lanzhou University(Natural Sciences)
基金 Supported by Association Franco-Chinoise pour la Recherche Scientinque and Techinque(AFCRST)and National Natural Science Foundation of China(20275014)
关键词 QSAR 利球性 中草药 分子结构参数 QSAR diuretic activity traditional Chinese medicinal herbs molecular descriptors
分类号 TP183 [自动化与计算机技术—控制理论与控制工程] O641 [理学—物理化学]
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参考文献13

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同被引文献8

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  • 8吴新根,柏毅.一种基于概率神经网络模型的胃病诊断专家系统[J].东南大学学报（自然科学版）,1999,29(4):57-62. 被引量：6

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兰州大学学报（自然科学版）
2006年 第2期

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