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定量结构保留关系
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  quantitative structure-retention relationships
     Application of Quantum-Chemical Descriptors in Quantitative Structure-Retention Relationships Studies of Chromatography
     量化参数在色谱定量结构—保留关系研究中的应用
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  相似匹配句对
     On quantitative structure-retention relationship for quinazoline derivatives
     喹唑啉类衍生物定量结构保留关系的研究
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     Quantitative structure-retention relationships for cyclic alcohol and ester compounds
     环醇和酯的定量结构-保留关系的研究
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     Studies on Quantitative Chromatographic Retention-Structure Relationships
     色谱保留时间-结构参数定量关系研究
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     Quantitative Relationship Between Gas Chromatographic Retention Indices and Structural Parameters of Polychlorinated Naphthalenes
     多氯萘的气相色谱保留指数与其结构参数的定量关系
短句来源
     Application of Quantum-Chemical Descriptors in Quantitative Structure-Retention Relationships Studies of Chromatography
     量化参数在色谱定量结构保留关系研究中的应用
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  quantitative structure-retention relationships
Quantitative Structure-Retention Relationships Applied to Reversed-Phase High-Perfromance Liquid Chromatography
      
A quantitative structure-retention relationships (QSRR) approach, as one of the all-important areas in modern chemical science, gives knowledge that is practical and necessary for drug design, combinatorial, and medicinal chemistries.
      
The results broaden the theoretical basis for the development of quantitative structure-retention relationships to be used for the prediction or interpretation of retention data in reversed-phase chromatography.
      
Quantitative structure-retention relationships for the investigation of the retention mechanism in high performance liquid chrom
      
Polyethylene-coated silica and zirconia stationary phases in view of quantitative structure-retention relationships
      
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AIM\ Fuzzy neural networks (FNNs) were applied to the study of q u antitative structure-retention relationship (QSRR) for RP-HPLC analysis of dru gs. METHODS\ Based on a thorough and systematic study on the molecula r structure of 162 drugs. FNNs were used to study QSRR of RP-HPLC. RESULT S\ Fuzzy rules reflecting the relationship among molecular structure, solven t strength and capacity factors were obtained automatically, and FNNs were succe ssfu lly used to predict the solvent strength and capacity factors...

AIM\ Fuzzy neural networks (FNNs) were applied to the study of q u antitative structure-retention relationship (QSRR) for RP-HPLC analysis of dru gs. METHODS\ Based on a thorough and systematic study on the molecula r structure of 162 drugs. FNNs were used to study QSRR of RP-HPLC. RESULT S\ Fuzzy rules reflecting the relationship among molecular structure, solven t strength and capacity factors were obtained automatically, and FNNs were succe ssfu lly used to predict the solvent strength and capacity factors (k′). The results of tests are satisfied. CONCLUSION\ The application of FNNs in the st udy of QSRR for RP-HPLC analysis had practical value in use.

目的 应用模糊神经网络研究药物反相色谱的定量结构 -保留关系 (QSRR)。方法 在全面考察药物分子结构参数的基础上 ,使用模糊神经网络对 1 62个药物进行 QSRR的研究。结果 应用模糊神经网络成功地实现了溶剂强度和容量因子的预测 ,并获得了药物分子结构参数与反相色谱流动相溶剂强度和容量因子之间关系的模糊规则 ,实验验证的结果也比较理想。结论 模糊神经网络可较满意地用于药物反相色谱 QSRR的研究

Chemical structures of polychlorinated dibenzodioxins (PCDDs) congeners are described by a novel modified molecular distance_edge vector (VMDE) called molecular electronegativity distance_edge vector (VMED, μ in short), developed in the athors's laboratory, which consists of the modified MED parameters based on the identical group as a peusdo atom instead of a traditional atom. Then quantitative structure_retention relationships (QSRRs) between the new VMDE parameters and gas chromatographic (GC) retention...

Chemical structures of polychlorinated dibenzodioxins (PCDDs) congeners are described by a novel modified molecular distance_edge vector (VMDE) called molecular electronegativity distance_edge vector (VMED, μ in short), developed in the athors's laboratory, which consists of the modified MED parameters based on the identical group as a peusdo atom instead of a traditional atom. Then quantitative structure_retention relationships (QSRRs) between the new VMDE parameters and gas chromatographic (GC) retention behavior of PCDDs are generated by multiple linear regression method (MLR) for non_polar, moderately polar, and polar stationary phases. All (QSRRs) models with high correlation (R>0.99) are developed for non_polar columns and moderately polar columns and polar columns (DB_5, SP_2100, SE_54, OV_1701). Then cross validation with leave_one out of procedure (CV) is performed with high correlation. The obtained satisfactory results show that the new μ vector is adapt to characterize chemical structure and to model the gas chromatographic retention behavior of PCDDs on various polar stationary phases.

在烷烃分子距边矢量的基础上 ,提出一种以各种非氢原子为基准的分子距离边数矢量(VMDE ;μ矢量 ) ,表征了环境有毒物二的分子结构并借助多元线性回归方法分别建立了二在非极性与极性色谱柱上的色谱保留指数或相对保留时间与其结构表征参数 μ矢量间的定量结构保留关系(QSRR)模型 .在非极性与极性色谱柱 (DB - 5 ,SP - 2 10 0 ,SE - 5 4 ,OV - 170 1)上色谱相对保留时间的QSRR模型的线性相关系数均在 0 .93以上 ,高达 0 9985。为检验模型的稳定性和预测能力 ,还进行了留一法交互校验 (crossvalidationwithleave -one -outofprocedure) ,结果优良。

Comparative molecular field analysis (CoMFA) was used to perform a quantitative structure retention relationship study on resolution of a series of fifty 5 arylhydantoins with the Pirkle type chiral stationary phase column. Satisfactory results were obtained after All Space Searching (ASS), with the leave one out cross validation correlation coefficient of CoMFA q 2=0.764 and conventional r 2=0.962. The model will be useful to speculate the combining mode between the chiral stationary phase and...

Comparative molecular field analysis (CoMFA) was used to perform a quantitative structure retention relationship study on resolution of a series of fifty 5 arylhydantoins with the Pirkle type chiral stationary phase column. Satisfactory results were obtained after All Space Searching (ASS), with the leave one out cross validation correlation coefficient of CoMFA q 2=0.764 and conventional r 2=0.962. The model will be useful to speculate the combining mode between the chiral stationary phase and the analogues as well as to quantitatively prognosticate the resolution of other 5 arylhydanoin analogues on Pirkle type chiral stationary phases.

用比较分子场分析法 (CoMFA)研究了 5 芳基乙内酰脲类化合物定量结构 -保留关系 .本研究从构象搜索所得的低能结构出发构建化合物分子的构象 ,并进行了全空间搜索 .得到了较好的模型 (CoMFA的交叉验证回归系数q2 为 0 .764 ,模型的线性回归系数r2 为 0 .962 ) .这些方程不仅有助于推测被识别剂和识别剂之间的结合方式 ,还可以定量地预测结构相近的类似物的拆分可能性 ,为设计合成新的识别剂和被识别剂都提供了一定的理论依据

 
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