应用一种反映分子局部微环境描述子--原子电性相互作用矢量(vector of atomic elec-tronegative interaction,AEIV)和原子杂化状态指数(Atomic Hybridation State Index,AHSI)对饱和脂肪酮类化合物的55种分子中的153个13C NMR谱建模模拟...应用一种反映分子局部微环境描述子--原子电性相互作用矢量(vector of atomic elec-tronegative interaction,AEIV)和原子杂化状态指数(Atomic Hybridation State Index,AHSI)对饱和脂肪酮类化合物的55种分子中的153个13C NMR谱建模模拟,应用多元线性回归方法得到定量结构波谱关系(QSSR)模型的复相关系数RMM=0.997,标准偏差为SDMM=7.155.采用留一法交互检验的结果是RCV=0.993,SDCV=10.195.并随机抽出三部分分子进行检验,得到的相关系数分别是RMM1=0.996,RMM2=0.996,RMM3=0.999.研究结果表明使用AEIV和AHSI所建模型预测能力是相当稳定的.展开更多
Atomic electronegativity interaction vector (AEIV) and atomic hybridization state index (AHSI) were used for establishing the quantitative structure-spectroscopy relationship(QSSR) model of 13C NMR chemical shifts of ...Atomic electronegativity interaction vector (AEIV) and atomic hybridization state index (AHSI) were used for establishing the quantitative structure-spectroscopy relationship(QSSR) model of 13C NMR chemical shifts of isodon diterpenoid compounds.Multiple linear regression (MLR) and computational neural network (CNN) were used to create the models,and the estimation stability and generalization ability of the models were strictly analyzed by both internal and external validations.The established MLR and CNN models were correlated with experimental values and the correlation coefficients of model estimation,leave-one-out (LOO)cross-validation (CV),and predicted values of external samples were Rcum=0.9724,RCV=0.9723,Qext=0.9738 (MLR);Rcum=0.9957,Qext=0.9956 (CNN),respectively.The results indicated that CNN gave significantly better prediction of 13C NMR chemical shifts for isodon diterpenoids than MLR.Satisfactory results showed that AEIV and AHSI were obviously good for modeling 13C NMR chemical shifts of isodon diterpenoid compounds.展开更多
将一种反映分子局部微环境描述子——原子电性相互作用矢量(Vector of atomic electronegative in-teraction,VAEI)和原子杂化状态指数(Atomic Hybridation State Index,AHSI)用于对位壬基酚(p-NP)异构体的共60个13C-NMR谱建模模拟,应...将一种反映分子局部微环境描述子——原子电性相互作用矢量(Vector of atomic electronegative in-teraction,VAEI)和原子杂化状态指数(Atomic Hybridation State Index,AHSI)用于对位壬基酚(p-NP)异构体的共60个13C-NMR谱建模模拟,应用多元线性回归方法得到定量结构波谱关系(QSSR)模型的复相关系数(RM1)为0.996,标准误差(SDM1)为4.982.采用留一法交互检验的复相关系数(RCV1)为0.994,标准误差(SDCV1)为5.505.随机抽出两部分样本进行检验,得到测试集的复相关系数(Rest1及Rest2)分别为0.996和0.995,标准误差(SDest1及SDest2)分别为5.005和5.322.研究结果表明,使用VAEI和AHSI所建模型具有良好的预测能力和稳定性.展开更多
文摘应用一种反映分子局部微环境描述子--原子电性相互作用矢量(vector of atomic elec-tronegative interaction,AEIV)和原子杂化状态指数(Atomic Hybridation State Index,AHSI)对饱和脂肪酮类化合物的55种分子中的153个13C NMR谱建模模拟,应用多元线性回归方法得到定量结构波谱关系(QSSR)模型的复相关系数RMM=0.997,标准偏差为SDMM=7.155.采用留一法交互检验的结果是RCV=0.993,SDCV=10.195.并随机抽出三部分分子进行检验,得到的相关系数分别是RMM1=0.996,RMM2=0.996,RMM3=0.999.研究结果表明使用AEIV和AHSI所建模型预测能力是相当稳定的.
文摘Atomic electronegativity interaction vector (AEIV) and atomic hybridization state index (AHSI) were used for establishing the quantitative structure-spectroscopy relationship(QSSR) model of 13C NMR chemical shifts of isodon diterpenoid compounds.Multiple linear regression (MLR) and computational neural network (CNN) were used to create the models,and the estimation stability and generalization ability of the models were strictly analyzed by both internal and external validations.The established MLR and CNN models were correlated with experimental values and the correlation coefficients of model estimation,leave-one-out (LOO)cross-validation (CV),and predicted values of external samples were Rcum=0.9724,RCV=0.9723,Qext=0.9738 (MLR);Rcum=0.9957,Qext=0.9956 (CNN),respectively.The results indicated that CNN gave significantly better prediction of 13C NMR chemical shifts for isodon diterpenoids than MLR.Satisfactory results showed that AEIV and AHSI were obviously good for modeling 13C NMR chemical shifts of isodon diterpenoid compounds.
文摘将一种反映分子局部微环境描述子——原子电性相互作用矢量(Vector of atomic electronegative in-teraction,VAEI)和原子杂化状态指数(Atomic Hybridation State Index,AHSI)用于对位壬基酚(p-NP)异构体的共60个13C-NMR谱建模模拟,应用多元线性回归方法得到定量结构波谱关系(QSSR)模型的复相关系数(RM1)为0.996,标准误差(SDM1)为4.982.采用留一法交互检验的复相关系数(RCV1)为0.994,标准误差(SDCV1)为5.505.随机抽出两部分样本进行检验,得到测试集的复相关系数(Rest1及Rest2)分别为0.996和0.995,标准误差(SDest1及SDest2)分别为5.005和5.322.研究结果表明,使用VAEI和AHSI所建模型具有良好的预测能力和稳定性.
