脂肪醇气相色谱保留指数与结构的相关性研究

在分子图的邻接矩阵基础上,构建了一个化合物均价连接性指数mL,mL=∑(Ai·Aj·Ak…)-0.5,其中一阶指数1L及定位基参数β与25种脂肪醇在6种固定相(SE-30,OV-3,OV-7,OV-11,OV-17和OV-25)上的气相色谱保留指数I显著相关,相关系数均大于0.98。所建定量结 构-保留关系(QSRR)模型具有良好的稳定性和预测能力,较好地揭示了脂肪醇在不同固定相上气相色谱保留指数的变化规律。     

Optimized partition of minimum spanning tree for piecewise modeling by particle swarm algorithm. QSAR studies of antagonism of angiotensin II antagonists

In quantitative structure-activity relationship (QSAR) modeling, when compounds in a training set exhibit a significant structural distinction between each other, in particular when chemicals of biological interest interacting on the receptor involve a different mechanism, it might be difficult to construct a single linear model for the whole population of compounds of interest with desired residuals. Developing a piecewise linear local model can be effective to circumvent the aforementioned problem. In this paper, piecewise modeling by the particle swarm optimization (PMPSO) approach is applied to QSAR study. The minimum spanning tree is used for clustering all compounds in the training set to form a tree, and the modified discrete PSO is applied to divide the tree to find satisfactory piecewise linear models. A new objective function is formulated for searching the appropriate piecewise linear models. The proposed PMPSO algorithm was used to predict the antagonism of angiotensin II. The results demonstrated that PMPSO is useful for improvement of the performance of regression models.     

含氧化合物气相色谱保留指数的预测

通过对部分含氧化合物(醇、酯、醛、酮)在不同固定相不同柱温下的849个样本的气相色谱保留指数值(RI)与其部分参数:拓扑指数(mQ)、定位基参数(Sox)、固定液极性值(CP)及柱温(T)建立定量结构-色谱保留相关(QSRR)模型。分别利用多元线性回归(MLR)、偏最小二乘回归(PLSR)、人工神经网络(ANN)建模,同时采用内部及外部双重验证的办法对所得模型稳定性能进行深入分析和检验,建模计算值、留一法(LOO)交互检验(CV)预测值和外部样本预测值的复相关系数Rcum、QLOO和Rext分别为0.9832、0.9829和0.9836(MLR);0.9832、0.9830和0.9836(PLSR);0.9910、0.9909和0.9900(ANN)。结果表明:所建定量结构保留关系(QSRR)模型具有良好的稳定性和预测能力,较好地揭示了含氧化合物(醇、酯、醛、酮)在不同色谱条件下气相色谱保留指数的变化规律。     

烯烃气相色谱保留指数的QSRR研究

通过对184个烯烃类化合物在不同固定相不同柱温下的617个样本的气相色谱保留指数值(RI)与其部分参数:拓扑指数(mQ)、偶极矩(DPL)、固定液极性值(CP)及柱温(T)建立定量-色谱保留相关(QSRR)模型.分别利用多元线性回归(MLR)、偏最小二乘回归(PLSR)、人工神经网络(ANN)建模,同时采用内部及外部双重验证的办法对所得模型稳定性能进行深入分析和检验,建模计算值、留一法(LOO)交互检验(CV)预测值和外部样本的复相关系数Rcum,QLOO和Rext分别为0.999 2,0.998 4和0.999 2(MLR);0.999 0,0.998 0和0.999 1(PLSR);0.999 4,0.998 7和0.999 2(ANN).结果表明:所建定量结构保留关系(QSRR)模型具有良好的稳定性和预测能力,较好地揭示了烯烃类化合物在不同固定相不同柱温上气相色谱保留指数的变化规律.     

Topological indices and QSPR analysis of some chemical structures applied for the treatment of heart patients

In this study, various beta-blocker drugs used for heart disease were analyzed, and their degree-based topological indices derived from the M-polynomial were calculated. Linear and quadratic regression analysis was used to obtain quantitative structure-property relationship models between the topological indices and eight the physicochemical properties of the drugs to determine their effectiveness. The results show that the harmonic index was the best predictor for boiling point, flashpoint, and enthalpy of vaporization, while the redefined third Zagreb index was effective for polarizability, molar refractivity, and molar volume. The inverse sum indeg index was found to be effective for molar refractivity, and the second modified Zagreb index was surface tension in linear regression models. In addition, the redefined third Zagreb index was the best predictor for polarizability and molar refractivity, while the second modified Zagreb index was effective for molar volume. The SDD index was found to be effective for surface tension in quadratic regression models.     

Using the Dugdale approximation to match a specific interaction in the adhesive contact of elastic objects

In the Maugis-Dugdale model of the adhesive contact of elastic spheres, the step cohesive stress sigma(0) is arbitrarily chosen to be the theoretical stress sigma(th) to match that of the Lennard-Jones potential. An alternative and more reasonable model is proposed in this paper. The Maugis model is first extended to that of arbitrary axisymmetric elastic objects with an arbitrary surface adhesive interaction and then applied to the case of a power-law shape function and a step cohesive stress. A continuous transition is found in the extended Maugis-Dugdale model for an arbitrary shape index n. A three-dimensional Johnson-Greenwood adhesion map is constructed. A relation of the identical pull-off force at the rigid limit is required for the approximate and exact models. With this requirement, the stress sigma(0) is found to be k(n)Deltagamma/z(0), where k(n) is a coefficient, Deltagamma the work of adhesion, and z(0) the equilibrium separation. Hence we have sigma(0) = 0.588Deltagamma/z(0), especially for n=2. The prediction of the pull-off forces using this new value shows surprisingly better agreement with the Muller-Yushchenko-Derjaguin transition than that using sigma(th) = 1.026Deltagamma/z(0), and this is true for other values of shape index n.     

Molecular Distance‐Edge Vector (μ) and Chromatographic Retention Index of Alkanes

A new method of quantitative structure‐retention relationship (QSRR) is proposed for estimating and predicting gas chromatographic retention indices of alkanes by using a novel molecular distance‐edge vector, called μ vector, containing 10 elements. The QSRR model (Ml), between the μ vector and chromatographic retention indices of 64 alkanes, was developed by using multiple linear regression (MLR) with the correlation coefficient being R = 0.9992 and the root mean square (RMS) error between the estimated and measured retention indices being RMS = 5.938. In order to explain the equation stability and prediction abilities of the M1 model, it is essential to perform a cross‐validation (CV) procedure. Satisfactory CV results have been obtained by using one external predicted sample every time with the average correlation coefficient being R = 0.9988 and average RMS = 7.128. If 21 compounds, about one third drawn from all 64 alkanes, construct an external prediction set and the 43 remaining construct an internal calibration set, the second QSRR model (M2) can be created by using calibration set data with statistics being R = 0.9993 and RMS = 5.796. The chromatographic retention indices of 21 compounds in the external testing set can be predicted by the M2 model and good prediction results are obtained with R = 0.9988 and RMS = 6.508.     

饱和醇定量结构-保留相关研究中人工神经网络的应用

以拓扑指数（分子连接性指数）为结构描述符,用人工神经网络技术建立了醇类化合物的结构与色谱保留值的相关性模型。研究了网络构造对模型稳定性的影响,考察了模型在单一固定相上及固定相上的适应性。与多元线性回归法相比较,人工神经网络模型具有更好的预测结果,但外推能力较弱。     

新型原子类型拓扑指数模拟含氧化合物气相色谱保留指数

Quantitative structure-retention relationship (QSRR) model for the estimation of retention indices (RIs)of 39 oxygen-containing compounds containing ketones and esters was established by our newly introduced distance-based atom-type indices DAI. The useful application of the novel DAI indices has been demonstrated by developing accurate predictive equations for gas chromatographic retention indices. The statistical results of the multiple linear regression for the final model are r=0.9973 and s=8.23. Furthermore, an external test set of 10 oxo-containing compounds can be accurately predicted with the final equation giving the following statistical results: r pred=0.9966 and s pred=8.56.     

The predicting study for chromatographic retention index of saturated alcohols by MLR and ANN

A QSRR method was followed to relate the observed Kovats retention indexes of saturated alcohol compounds with their molecular connectivity indices by means of multilinear regression analysis and artificial neural networks technique. The alcohols included linear, branched with hydroxyl group on a primary, secondary, or tertiary carbon atom. At first, models were generated for six OV (Ohio Valley) series columns separately, with high value of R and F statistics. Then a combined model, added a polarity term of stationary phase (M), was also developed for all these columns, and the result was satisfactory. For comparison, the neural network of BP algorithm was applied, and it was found that the neural network could exceed the level of the multiple regression method. The stability and validity of both models were tested by cross-validation technique and by prediction response values for the prediction set.     

Piecewise hypersphere modeling by particle swarm optimization in QSAR studies of bioactivities of chemical compounds

As the structural diversity in a quantitative structure-activity relationship (QSAR) model increases, constructing a good model becomes increasingly difficult, and simply performing variable selection might not be sufficient to improve the model quality to make it practically usable. To combat this difficulty, an approach based on piecewise hypersphere modeling by particle swarm optimization (PHMPSO) is developed in this paper. It treats the linear models describing the sought-for subsets as hyperspheres which have different radii in the data space. According to the attribute of each hypersphere, all compounds in the training set are allocated to hyperspheres to construct submodels, and particle swarm optimization (PSO) is applied to search the optimal hyperspheres for finding satisfactory piecewise linear models. A new objective function is formulated to determine the appropriate piecewise models. The performance is assessed using three QSAR data sets. Experimental results have shown the good performance of this technique in improving the QSAR modeling.     

Micellar electrokinetic chromatography as a fast screening method for the determination of 1,4-dihydropyridine calcium antagonists

A micellar electrokinetic chromatographic method (MEKC) was optimized for the separation of six calcium antagonists. The effects of the buffer (concentration and pH), concentration of sodium dodecyl sulphate (SDS), the organic modifier, the injection time, and the voltage applied were studied. A final appropriate electrolyte of 50 mM borate buffer, pH 8.2, containing 20 mM SDS and 15% (v/v) acetonitrile was found to provide the optimum separation with respect to resolution and migration time. The samples were introduced hydrostatically for 4 s at 50 mbar injection pressure and the applied voltage was +25 kV. The screening of the six compounds was achieved in less than 15 min: nifedipine (migration time, tm = 6.9 min), nimodipine (tm = 10.1 min), felodipine (tm = 12.2 min), nicardipine hydrochloride (tm = 12.7 min), lacidipine (tm = 13.5 min) and amlodipine besylate (tm = 14.1 min, tm = 8 min). The method developed showed to be linear at least up to 70 micrograms/ml with a detection limit of about 5 micrograms/ml for each compound. The within-day and inter-day area reproducibility (R.S.D.) were, respectively, lower than 4.8 and 8.6% for six replicate samples.