Prediction of n-octanol/water partition coefficients from PHYSPROP database using artificial neural networks and E-state indices

A new method, ALOGPS v 2.0 (http://www.lnh.unil.ch/~itetko/logp/), for the assessment of n-octanol/water partition coefficient, log P, was developed on the basis of neural network ensemble analysis of 12 908 organic compounds available from PHYSPROP database of Syracuse Research Corporation. The atom and bond-type E-state indices as well as the number of hydrogen and non-hydrogen atoms were used to represent the molecular structures. A preliminary selection of indices was performed by multiple linear regression analysis, and 75 input parameters were chosen. Some of the parameters combined several atom-type or bond-type indices with similar physicochemical properties. The neural network ensemble training was performed by efficient partition algorithm developed by the authors. The ensemble contained 50 neural networks, and each neural network had 10 neurons in one hidden layer. The prediction ability of the developed approach was estimated using both leave-one-out (LOO) technique and training/test protocol. In case of interseries predictions, i.e., when molecules in the test and in the training subsets were selected by chance from the same set of compounds, both approaches provided similar results. ALOGPS performance was significantly better than the results obtained by other tested methods. For a subset of 12 777 molecules the LOO results, namely correlation coefficient r(2)= 0.95, root mean squared error, RMSE = 0.39, and an absolute mean error, MAE = 0.29, were calculated. For two cross-series predictions, i.e., when molecules in the training and in the test sets belong to different series of compounds, all analyzed methods performed less efficiently. The decrease in the performance could be explained by a different diversity of molecules in the training and in the test sets. However, even for such difficult cases the ALOGPS method provided better prediction ability than the other tested methods. We have shown that the diversity of the training sets rather than the design of the methods is the main factor determining their prediction ability for new data. A comparative performance of the methods as well as a dependence on the number of non-hydrogen atoms in a molecule is also presented.     

芳香族化合物生物降解性的QSBR研究

分别采用线性基团贡献法和人工神经网络法对芳香族化合物的生物降解最大去除率QTOD进行QSBR研究。得到不同基团对生物降解性的贡献顺序为 :C6H5>COOH >OH >CO >CH3 >C1 >NH2>NO2 。线性基团贡献法对于训练组和测试组的预测正确率分别为 86%和 80 % ,总的预测正确率达85 % ;而人工神经网络法的预测正确率分别为 94%、80 %和 92 %。结果表明 ,线性基团贡献法和神经网络法的预测效果均很好 ,而神经网络法的预测更精确。     

酚类化合物的多元回归分析及神经网络法研究

在药物分子设计的诸多参量中，化合物在正辛酸／水中的分配系数的对数lgP是广为应用的一个参量（称为流水性参数）．lgP反映了化合物分子脂溶性的大小·药物分子具有适当的lgP值才能发挥最大的活性·lgP值的实验测定虽然并不困难，但对干至今仍未测定lgP值的化合物或在药物分子     

A method for measuring infinite-dilution partition coefficients of volatile compounds between the gas and liquid phases of aqueous systems

Vejrosta, J., Novák, J. and Jönsson, J.Å., 1982. A method for measuring infinite-dilution partition coefficients of volatile compounds between the gas and liquid phases of aqueous systems. Fluid Phase Equilibria, 8: 25–35.A method has been developed for measuring the partition coefficients of volatile compounds between the gas and liquid phases of aqueous systems, based on the direct analysis of both phases. A gas mixture containing a known proportion of a volatile compound is drawn through the liquid (water) until equilibrium is established. A defined volume of the liquid phase is then withdrawn through a porous-polymer trap while maintaining the system at equilibrium. The residual water in the trap is then expelled by a stream of nitrogen gas, and the deposit remaining is thermally desorbed and analyzed by gas chromatography. This approach, together with an experimental technique for producing gas mixtures containing an accurately known concentration of hydrocarbon at low values, makes it possible to determine accurately the partition coefficients of low-solubility compounds, such as for hydrocarbons in aqueous systems, at very low solute concentrations in the system. The method has been verified by measuring the partition coefficient of hexane between the gas and liquid phases of an aqueous system at various concentrations and temperatures.     

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.