三嗪类化合物溶解度参数及毒性构－效关系

测定了１２种三嗪类化合物的水溶解度，辛醇水分配系数和对发光菌的毒性，并用分子连结性指数建立了预测三嗪类化合物的溶解度，辛醇水分配系数及对发光菌毒性的定量结构活性相关方程，其中１０种化合物文献中未见报道。     

基于DFT和分子连接性指数方法研究醇类化合物的水溶解度和分配系数

将DFT方法计算得到的量化参数和分子连接性指数联合应用到60个醇类化合物的溶解度和辛醇/水分配系数的QSPR研究中,分别通过逐步回归得到具有显著统计意义的4个参数和5个参数的QSPR方程.以此4个参数和5个参数分别作为输入参数,采用BPNN,RBFNN方法建立了QSPR预测模型,使用Latin-partition交叉验证方法评价模型的预测能力.BPNN,RBFNN模型对溶解度预测的相关系数分别为0.993和0.994,而对辛醇/水分配系数预测的相关系数分别0.990和0.997,结果令人满意.     

三嗪类化合物溶解度参数及毒性构—效关系

测定了１２种三嗪类化合物的水溶解度，辛醇水分配系数和对发光菌的毒性，并用分子连结性指数建立了预测三嗪类化合物的溶解度，辛醇水分配系数及对发光菌毒性的定量结构活性相关方程，其中１０种化合物献中未见报道。e     

中空纤维膜液相微萃取-高效液相色谱测定有机化合物正辛醇/水分配系数

提出了一种中空纤维膜液相微萃取-高效液相色谱直接测定有机化合物正辛醇/水分配系数的新方法,并用该方法测定了不同脂溶性有机化合物的正辛醇/水分配系数。由于中空纤维膜液相微萃取有机萃取剂用量很少,故能显著提高萃取时的传质速度,缩短萃取时间。正辛醇装入中空纤维膜内,在萃取过程中,正辛醇相和水相不会形成乳化层。萃取完成后,可直接取出正辛醇相的样品进行分析,lgK测定能在30 m in内完成。本研究对6种化合物进行了测定,测定结果用文献报道值和经典摇瓶法进行了验证。表明方法快速、准确、样品消耗量少。     

修正的分子连接性指数mL用于氯代苯、醇、酯的QSAR/QSRR研究

在分子拓扑理论的基础上，提出了1个改进的连接性指数气^mL并用气研究了氯代苯、醇、酯的正辛醇-水分配系数及气相色谱保留指数，^mL不仅对这些有机化合物有良好的结构选择性，而且与其正辛醇-水分配系数、气相色谱保留指数有良好的相关性；由方程得出的预测值与实验测定值之间能较好地吻合。     

苯系物Lewis酸碱性的定量化尝试及估算

Lewis酸碱理论研究目前尚处于定性阶段,在环境化学上应用甚少,Gutmann等曾采用热力学与~(31)P NMR方法定量了少量有机溶剂的酸碱性,Kamlet等也利用~(19)F NMR测定了一些有机物的碱性.本文提出了一种利用多种溶剂/水分配系数来定量苯系物酸碱性的方法,该法简便、快速,有广泛的适用性。     

含氧有机物脂水分配系数与结构参数的关系

应用半经验量子化学AM1方法得到了108种含氧有机分子的优势构象,利用AM1方法和分子图形学技术获得了它们的电子结构和几何结构参数,并将这些参数与脂水分配系数相关联,采用逐步回归分析方法,成功地建立了拟合度高、物理意义明确、预测能力强的含氧有机分子脂水分配系数-结构参数定量关系方程,找出了影响含氧有机分子脂水分配系数的主要结构因素.     

脂质体电动色谱法评价阿魏酸与生物膜的相互作用

利用脂质体与生物膜结构的相似性,将脂质体加入毛细管电泳缓冲溶液中作为假固定相,在数分钟内测定了阿魏酸的脂水分配系数Klw,建立了脂质体电动色谱评价阿魏酸与生物膜相互作用的方法。研究了脂质体中胆固醇的含量、缓冲溶液pH值和缓冲体系对Klw的影响。结果表明,在实验条件范围内(胆固醇含量0~30%,pH值4.0~12.0),胆固醇含量升高,缓冲溶液pH值增大,Klw降低;在不同的缓冲体系中,离子强度越大,Klw越大。脂水分配系数的变化反映了阿魏酸与生物膜相互作用。     

反相高效液相色谱法测定某些有机化合物的分配系数

有机化合物的分配系数(K_(ow))是表示有机物质疏水性或亲脂性的程度,是评价农药和工业化合物对环境潜在性危害的重要物理化学参数。用K_(ow)还可推测有机化合物的水的溶解度、土壤吸附系数、生物浓缩趋势以及在自然环境中的降解速率等。所以K_(ow)无论是在对有机物对环境潜在性危害的评价,还是在总结化学结构与生物活性关系中都是重要的指     

分子亲脂-亲水性的量子化学描述II. 氨基酸侧链的亲水指标和亲脂指标

在启发式亲脂势HMLP(heuristicmolecularlipophilicitypotential)的基础上提出了分子、分子片段和原子的亲水指标和亲脂指标.计算出了20个天然氨基酸侧链的亲水、亲脂指标和亲水、亲脂表面积,并用线性自由能函数表达氨基酸侧链的溶剂化自由能,?Gsol,=b0 b1Li b2Hi b3Si b4Si.应用线性自由能函数和氨基酸侧链的亲水和亲脂! -i指标,计算了20个氨基酸残基的3种相转移自由能(蒸气-水、蒸气-正辛醇、正辛醇-水)和正辛醇-水分配系数logPow,取得了与实验值高度一致的良好效果.HMLP的亲水和亲脂指标是HMLP的指标化,扩展了这一方法的使用范围.氨基酸侧链的亲水、亲脂指标和线性自由能函数有望用于生物大分子受体与配体的结合自由能的估算、蛋白质的结构与功能、蛋白-蛋白相互作用和识别的研究.     

Prediction of drug solubility by the general solubility equation (GSE)

The revised general solubility equation (GSE) proposed by Jain and Yalkowsky is used to estimate the aqueous solubility of a set of organic nonelectrolytes studied by Jorgensen and Duffy. The only inputs used in the GSE are the Celsius melting point (MP) and the octanol water partition coefficient (K(ow)). These are generally known, easily measured, or easily calculated. The GSE does not utilize any fitted parameters. The average absolute error for the 150 compounds is 0.43 compared to 0.56 with Jorgensen and Duffy's computational method, which utilitizes five fitted parameters. Thus, the revised GSE is simpler and provides a more accurate estimation of aqueous solubility of the same set of organic compounds. It is also more accurate than the original version of the GSE.     

Prediction of extraction efficiency in supported liquid membrane with a stagnant acceptor phase by means of artificial neural network

An artificial neural network model of supported liquid membrane extraction process with a stagnant acceptor phase is proposed. Triazine herbicides and phenolic compounds were used as model compounds. The model is able to predict the compound extraction efficiency within the same family based on the octanol–water partition coefficient, water solubility, molecular mass and ionisation constant of the compound. The network uses the back‐propagation algorithm for evaluating the connection strengths representing the correlations between inputs (octanol–water partition coefficients logP, acid dissociation constant pK_a, water solubility and molecular weight) and outputs (extraction efficiency in dihexyl ether and undecane as organic solvents). The model predicted results in good agreement with the experimental data and the average deviations for all the cases are found to be smaller than ±3%. Moreover, standard statistical methods were applied for exploration of relationships between studied parameters.     

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.     

Development of an automatic estimation system for both the partition coefficient and aqueous solubility

Summary A computer program has been developed for estimating both the partition coefficient between 1-octanol and water phases and the aqueous solubility from the structural formula. This system is an extended version of a previously described program entitled CHEMICALC for the automatic estimation of the partition coefficient. The aqueous solubility is estimated via two pathways. The first is based on the linear relationship between logarithms of the aqueous solubilities of 497 compounds and their estimated 1-octanol/water partition coefficients. In the second, combined handling of two available group contribution methods of Irmann [Chem. Ing. Tech., 37 (1965) 789] and Wakita et al. [Chem. Pharm. Bull., 34 (1986) 4663] is adopted according to compound type. Some revisions and extensions of the methods for estimating the aqueous solubility have been made in both pathways, and the accuracy of the estimated aqueous solubilities for 497 compounds is discussed.     

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.