应用线性溶剂化能方法构建定量结构-性质相关(QSPR)模型。
Linear solvation energy technique was used to develop quantitative structure-property relationships (QSPR) model.
建立了一个基于人工神经网络方法的定量结构-性质相关性(QSPR)研究模型，用于预测脂肪醉闪点。
A quantitative structure-property relationship (QSPR) model based on artificial neural networks was established to predict the flash points of fatty alcohols.
结果表明，所建定量结构性质相关（QSPR）模型的相关性高、稳健性好、预测能力强。
These results demonstrate that the quantitative structure-property relationship(QSPR) models have a high correlativity, fine stability and precise predictability.
基于定量结构-活性相关(QSAR)研究硝基苯类化合物的性质具有重要意义。
Study on quantitative structure-activity relationship (QSAR) of nitro aromatic compounds would be helpful in researching for nitro aromatic activity.
基于定量结构-活性相关(QSAR)研究硝基苯类化合物的性质具有重要意义。
Study on quantitative structure-activity relationship (QSAR) of nitro aromatic compounds would be helpful in researching for nitro aromatic activity.
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