Prediction of aqueous solubility of organic compounds by the general solubility equation (GSE)

The revised general solubility equation (GSE) is used along with four different methods including Huuskonen's artificial neural network (ANN) and three multiple linear regression (MLR) methods to estimate the aqueous solubility of a test set of the 21 pharmaceutically and environmentally interesting compounds. For the selected test sets, it is clear that the GSE and ANN predictions are more accurate than MLR methods. The GSE has the advantages of being simple and thermodynamically sound. The only two inputs used in the GSE are the Celsius melting point (MP) and the octanol water partition coefficient (K(ow)). No fitted parameters and no training data are used in the GSE, whereas other methods utilize a large number of parameters and require a training set. The GSE is also applied to a test set of 413 organic nonelectrolytes that were studied by Huuskonen. Although the GSE uses only two parameters and no training set, its average absolute errors is only 0.1 log units larger than that of the ANN, which requires many parameters and a large training set. The average absolute error AAE is 0.54 log units using the GSE and 0.43 log units using Huuskonen's ANN modeling. This study provides evidence for the GSE being a convenient and reliable method to predict aqueous solubilities of organic compounds.     

Calculation of entropy and heat capacity of organic compounds in the gas phase. Evaluation of a consistent method without adjustable parameters. Applications to hydrocarbons

The purpose of this study has been to determine how well a consistent ab initio thermostatistical method reproduces experimental values of heat capacity and entropy. The method has been applied to calculation of heat capacity and entropy of a representative set of hydrocarbons that includes compounds consisting of multiple conformers. All Cp and S values are for the gaseous state at 1 atm; units are cal K-1 mol-1. A detailed sensitivity (error) analysis has been performed to determine the root mean square (rms) values of errors expected of the calculated values: these are 0.27 cal for Cp and 0.36 cal for entropy. In comparing calculated values with experimental values, it is necessary to consider also the uncertainties of the experimental data. When these are included, the expected rms values of Cp(experimental) - Cp(calculated) values at 298.15 K range from 0.21 to 0.73. For S(experimental) - S(calculated), they range from 0.36 to 0.72. Calculations with frequencies derived with the 6-31G(d,p) basis set and scaled by 0.91 yielded rms values for Cp(experimental) - Cp(calculated) of individual compounds from 0.14 to 0.84 cal and rms values for S(experimental) - S(calculated) of individual compounds from 0.07 to 1.11 cal. Calculated Cp values for 7 out of 16 compounds agree with experimental values within the rms uncertainty estimated for the compound, and 11 fall within twice that estimate. For entropy, the calculated values for 13 of 18 compounds agree with the very limited available experimental data within the rms estimated uncertainty for the compound, and 16 of 18 fall within twice the uncertainty.     

一种计算水溶解度的经验加合模型的适用范围与局限

我们发展了一种用于预测有机小分子化合物水溶解度(logS)的经验方法XLOGS. 它本质上是一种加合模型, 采用83种原子/基团类型和3个校正因子作为模型的描述符.该方法还可以根据一个合适的参照分子的logS实验值来计算未知化合物的logS值. 我们将XLOGS模型在由4171个化合物组成的训练集上进行了参数化, 多元线性回归获得的相关系数R²和标准偏差SD分别为0.82和0.96单位. 将该训练集进一步分为仅含液体化合物和仅含固体化合物的两个子集. XLOGS模型在这两个子集上的回归结果显示前者优于后者(标准偏差分别为0.65单位和0.94单位). 还利用log1/S和logP(脂水分配系数)之间的差值来研究XLOGS方法在液体和固体化合物数据集上的表现. 研究结果表明: XLOGS等加合法模型更适合应用于这两者差值接近于0的化合物. 我们还将XLOGS和其他三种流行的logS计算模型(包括Qikprop, MOE-logS和ALOGPS)在一个含有132个类药化合物的独立测试集上进行了比较. 总体而言, 我们的研究结果为加合法模型在水溶解度预测方面的合理应用提供了指导.     

Estimation of aqueous solubility of organic molecules by the group contribution approach. Application to the study of biodegradation.

A reliable and generally applicable aqueous solubility estimation method for organic compounds based on a group contribution approach has been developed. Two models have been established based on two different sets of parameters. One has a higher accuracy, while the other has a more general applicability. The prediction potentials of these two models have been evaluated through cross-validation experiments. For model I, the mean cross-validated r2 and SD for 10 such cross-validation experiments were 0.946 and 0.503 log units, respectively. While for model II, they were 0.953 and 0.546 log units, respectively. Applying our models to estimate the water solubility values for the compounds in an independent test set, we found that model I can be applied to 13 out of 21 compounds with a SD equal to 0.58 log unit and model II can be applied to all the 21 compounds with a SD equal to 1.25 log units. Our models compare favorably to all the current available water estimation methods. A program based on this approach has been written in FORTRAN77 and is currently running on a VAX/VMS system. The program can be applied to estimate the water solubility of the water solubility of any organic chemical with a good or fairly good accuracy except for except for electrolytes. Applying our aqueous solubility estimation models to biodegradation studies, we found that although the water solubility was not the sole factor controlling the rate of biodegradation, ring compounds with greater solubilities were more likely to biodegrade at a faster rate. The significance of the relationship between water solubility and biodegradation activity has been illustrated by predicting the biodegradation activity of 27 new chemicals based solely on their estimated solubility values.