Prediction of the logarithmic of partition coefficients (log P) of some organic compounds by least square-support vector machine (LS-SVM)

A new method least square-support vector machine (LS-SVM) was used to develop quantitative structure–property relationship (QSPR) models for predicting the logarithmic of n-octanol/water partition coefficient (log P) of some derivatives phenolic compounds. The calibration and predictive ability of LS-SVM were investigated and compared with those of three other methods; multiple linear regression (MLR), support vector linear regression (SVR) and artificial neural network (ANN). The results showed that the log P values calculated by LS-SVM were in good agreement with experimental values, and the performances of the LS-SVM models were comparable or superior to those of MLR, SVR and ANN methods. The root-mean-square errors of the training set and the predicting set for the LS-SVM model were 0.0855, 0.0746 and the squares of the correlation coefficients were 0.9960 and 0.9728, respectively. These values and other statistical parameters obtained for the LS-SVM model show the reliability of this model. LS-SVM is a new and effective method for predicting log P of some organic compounds, and can be used as a powerful chemometrics tool for QSPR studies.     

部分有机物蝌蚪麻醉活性的预测

基于定量结构-活性相关性（QSAR）原理,研究了49种有机化合物结构与其蝌蚪麻醉活性的内在定量关系。首先应用分子电性作用矢量（molecular electronegativity interaction vector,MEIV）表征49种有机化合物的结构,再采用多元线性回归（MLR）方法建立了相应的QSAR预测模型,最后对所建模型分别进行了内部验证和外部验证。所建模型的复相关系数(Rcum)、留一法(LOO)交互校验复相关系数(RCV)和外部样本校验复相关系数(Qext)分别为0.9415、0.9127和0.9253,证明该模型均具有较高的稳定性和预测能力。     

QSRR在化学和环境分析中应用研究进展

综述了QSRR中的多元线性回归（Multple Linear Regresion,MLR）、偏最小二乘回归（Partial Least Squares Regression,PLSR）、人工神经网络（Artificial Neural Networks,ANN）几种常用建模方法、分子描述符的选取的要点及原则及相关模型的建立和评价等方法在化学与环境化合物分析中的应用现状,并对QSRR在该领域的研究方向和发展前景进行了展望,以期为今后更深层次的研究作综合性的参考.     

酰肼类抗结核病药物的3D-QSAR研究

采用三维全息原子场作用矢量(3D-HoVAIF)对115个酰肼类抗结核病药物进行定量构效关系(QSAR)研究。运用多元线性回归(MLR)和偏最小二乘回归(PLS)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了深入分析和检验。MLR建模和PLS建模的复相关系数(Rcum2)、留一法交互校验复相关系数(QLOO2)以及外部样本校验复相关系数(Qext2)分别为0.765、0.663、0.748和0.733、0.614、0.715。结果表明,3D-HoVAIF能较好表征抗结核药物分子结构信息,所建QSAR模型具有良好稳定性和预测能力,为抗结核病药物的研发提供一定的理论基础。     

用PM3量化参数预测多氯代二苯并呋喃的logKow

从现有的PCDFs分子的正辛醇 /水分配系数 (logKow)实验数据出发 ,建立定量结构 性质关系方程(QSPR) .采用G98W程序包中的PM3方法对 13 5个多氯代二苯并呋喃 (PCDFs)分子和二苯并呋喃进行了优化计算 ,作业命令为 #pPM3optfreqscf(conver =9) ,以计算所得的分子轨道能量、碳原子电荷作为PCDFs分子结构描述符 ,运用多元线性回归技术建立了PCDFs的logKow与分子结构描述符的四元方程 ,最优相关系数为 0 .95 0 7,标准偏差为 0 .173 7,经检验该模型的稳健性好 ,并对未有实验数据的 85个PCDFs的logKow进行预测     

甾族化合物~(13)CNMR谱化学位移定量构谱关系的研究

利用原子电性距离矢量(Atomic Electro-negativity Distance Vector,AEDV)和原子杂化状态指数(Atomic Hybridization State Index,AHSI)对203个甾族化合物中4434个碳原子进行结构表征并与其核磁共振碳谱(13CNMR)建立多元线性定量结构波谱相关(QSSR)模型,同时运用逐步回归结合统计检测对模型变量进行筛选,最后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(leave-one-out,LOO)交互校验(cross-validation,CV)预测值和外部样本预测值的复相关系数(R)分别为0.9574、0.9571和0.9537.其结果表明,AEDV,AHSI与13CNMR谱化学位移显著相关.     

Estimating the sound absorption coefficients of perforated wooden panels by using artificial neural networks

Developing efficient sound absorption materials is a relevant topic for large scale structures such as gymnasiums, shopping malls, airports and stations. This study employs artificial neural network (ANN) algorithm to estimate the sound absorption coefficients of different perforated wooden panels with various setting combinations including perforation percentage, backing material and thickness. The training data sets are built by carrying out a series of experimental measurements in the reverberation room to evaluate the sound absorption characteristics of perforated wooden panels. A multiple linear regression (MLR) model is also developed for making comparisons with ANN. The analytical results indicate that the ANN exhibits satisfactory reliability of a correlation between estimation and truly measured absorption coefficients of approximately 0.85. However, MLR cannot be applied to nonlinear cases. ANN is a useful and reliable tool for estimating sound absorption coefficients estimation.     

5,6-二氢-2-吡喃酮作为HIV-1蛋白酶抑制剂的3D-QSAR和对接研究

本文采用分子对接法研究了24个5,6-二氢-2-吡喃酮衍生物与HIV-1蛋白酶的相互作用,并采用三维定量构效关系(3D-QSAR)研究了药物分子化学结构和生物活性之间的关系.最终得到模型的复相关系数(R_(cum)~2)为0.961、留一法交互校验复相关系数(Q_(cv)~2)为0.897,外部交互验证复相关系数(Q_(ext)~2)为0.880,从结果可以看出三维定量构效关系对化合物的抗艾滋病活性具有比较好的预测能力.然后通过分子对接研究了配体小分子和HIV-1蛋白酶活性氨基酸残基之间的结合模式,这些信息对于以后设计合成新的抗艾滋病药物具有一定的指导作用.     

Artificial neural network (ANN) approach for modeling and formulation of phenol adsorption onto activated carbon

In this study, a three-layer feed-forward back propagation network with Levenberg-Marquardt (LM) learning algorithm was applied to predict adsorption of phenol onto activated carbon (AC). Batch experiments were carried out to obtain experimental data. The neural network was trained considering the amount of adsorbent, initial concentration of phenol, temperature, contact time and pH as input parameters and the final concentration of phenol as a desired parameter. Different transfer functions for hidden and output layers and different number of neurons in a hidden layer were tested to optimize the network structure. An empirical equation for final concentration of phenol was developed by using the weights of optimized network. Accuracy of the developed ANN model was also measured using statistical parameters, such as mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE) and correlation coefficient (R²). Results showed that MAE, MSE, RMSE, and R² values of the ANN model were 0.1540, 0.0565, 0.2378, and 0.9998, respectively, which indicate high accuracy of the ANN model. In the equilibrium study, predicted results of the ANN model were also compared with experimental data and the results of other conventional isotherm models.