Prediction Of Aqueous Solubility Research Articles

Development of quantitative structure-property relationship models for early ADME evaluation in drug discovery. 1. Aqueous solubility.

A simple QSPR model, based on seven 1D and 2D descriptors and artificial neural network, was developed for fast evaluation of aqueous solubility. The model was able to predict the molar solubility of a diverse set of 1312 organic compounds with an overall correlation coefficient of 0.92 and a standard deviation of 0.72 log unit between the calculated and experimental data. Considering the fact that the estimated uncertainty of the experimental data is no less than 0.5 log unit, the results demonstrate that carefully chosen physically meaningful 1D and 2D descriptors encode sufficient molecular information for fast and reasonably reliable prediction of aqueous solubility with a simple neural network. As a comparison, we calculated the solubility of a test set of 258 compounds, ranging from simple hydrocarbons to more complex multifunctional organic molecules, with a commercial program (QMPR+ version 2.0.1 of SimulationPlus Inc.) and compared the results with predictions from our model. Statistical parameters indicate that for small and simple organic compounds, QMPR+ outperforms our model. However for more complex multifunctional molecules, our model is superior.

Estimation of aqueous solubility of chemical compounds using E-state indices.

The molecular weight and electrotopological E-state indices were used to estimate by Artificial Neural Networks aqueous solubility for a diverse set of 1291 organic compounds. The neural network with 33-4-1 neurons provided highly predictive results with r(2) = 0.91 and RMS = 0.62. The used parameters included several combinations of E-state indices with similar properties. The calculated results were similar to those published for these data by Huuskonen (2000). However, in the current study only E-state indices were used without need of additional indices (the molecular connectivity, shape, flexibility and indicator indices) also considered in the previous study. In addition, the present neural network contained three times less hidden neurons. Smaller neural networks and use of one homogeneous set of parameters provides a more robust model for prediction of aqueous solubility of chemical compounds. Limitations of the developed method for prediction of large compounds are discussed. The developed approach is available online at http://www.lnh.unil.ch/~itetko/logp.

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.

Prediction of aqueous solubility of heteroatom-containing organic compounds from molecular structure.

The use of quantitative structure-property relationships (QSPRs) to predict aqueous solubilities (log S) of heteroatom-containing organic compounds from their molecular structure is presented. Three data sets are examined. Data set 1 contains 176 compounds having one or more nitrogen atoms with some oxygen (log S[mol/L] range is -7.41 to 0.96). Data set 2 contains 223 compounds having one or more oxygen atoms, with no nitrogen (log S[mol/L] range is -8.77 to 1.57). Data set 3 contains all 399 compounds from sets 1 and 2 (log S/mol/L] range is -8.77 to 1.57). After descriptor generation and feature selection, multiple linear regression (MLR) and computational neural network (CNN) models are developed for aqueous solubility prediction. The best results were obtained with nonlinear CNN models. Root-mean-square (rms) errors for training with the three data sets ranged from 0.3 to 0.6 log units. All models were validated with external prediction sets, with the rms errors ranging from 0.6 log units to 1.5 log units.

Estimation of water solubility from atom‐type electrotopological state indices

Based on the atom-type electrotopological state (E-state) indices, a quantitative structure-property relationship model for the prediction of aqueous solubility for a diverse set of 745 organic compounds is presented. The multiple linear regression analysis was used to build the models. A training set of 674 compounds, containing 349 liquids and 325 solids and having a range of aqueous solubility (log S) values from 2.77 to 11.62, was obtained from the literature. For this set, the squared correlation coefficient and standard deviation for a linear model with 31 atom-type E-state indices and three simple correction factors were r2 = 0.94 and s = 0.58 (log units), respectively. The corresponding statistics for the test sets not included in the training set were, for a set of 50 pesticides, r2 = 0.79 and s = 0.81 and, for a set of 21 drug and pesticide compounds, r2 = 0.83 and s = 0.84, respectively. The contribution of melting points was also evaluated. The use of melting point increased the accuracy of the models in the fit of the training set but not in the prediction of the test sets. Hence, the proposed method offers fast and accurate estimation of aqueous solubility of organic compounds using atom-type E-state indices without the need of any experimental parameters like the melting points.

ESTIMATION OF WATER SOLUBILITY FROM ATOM-TYPE ELECTROTOPOLOGICAL STATE INDICES

Based on the atom-typee ectrotopological state (E-state) indices, a quantitative structure–property relationship model for the prediction of aqueous solubility for a diverse set of 745 organic compounds is presented. The multiple linear regression analysis was used to build the models. A training set of 674 compounds, containing 349 liquids and 325 solids and having a range of aqueous solubility (log S) values from 2.77 to —11.62, was obtained from the literature. For this set, the squared correlation coefficient and standard deviation for a linear model with 31 atom-type E-state indices and three simple correction factors were r2 = 0.94 and s = 0.58 (log units), respectively. The corresponding statistics for the test sets not included in the training set were, for a set of 50 pesticides, r2 = 0.79 and s = 0.81 and, for a set of 21 drug and pesticide compounds, r2 = 0.83 and s = 0.84, respectively. The contribution of melting points was also evaluated. The use of melting point increased the accuracy of the models in the fit of the training set but not in the prediction of the test sets. Hence, the proposed method offers fast and accurate estimation of aqueous solubility of organic compounds using atom-type E-state indices without the need of any experimental parameters like the melting points.

Simultaneous prediction of aqueous solubility and octanol/water partition coefficient based on descriptors derived from molecular structure.

It has been shown that water solubility and octanol/water partition coefficient for a large diverse set of compounds can be predicted simultaneously using molecular descriptors derived solely from a two dimensional representation of molecular structure. These properties have been modelled using multiple linear regression, artificial neural networks and a statistical method known as canonical correlation analysis. The neural networks give slightly better models both in terms of fitting and prediction presumably due to the fact that they include non-linear terms. The statistical methods, on the other hand, provide information concerning the explanation of variance and allow easy interrogation of the models. Models were fitted using a training set of 552 compounds, a validation set and test set each containing 68 molecules and two separate literature test sets for solubility and partition.

Prediction of aqueous solubility for a diverse set of organic compounds based on atom-type electrotopological state indices

We describe robust methods for estimating the aqueous solubility of a set of 734 organic compounds from different structural classes based on multiple linear regression (MLR) and artificial neural networks (ANN) model. The structures were represented by atom-type electrotopological state (E-state) indices. The squared correlation coefficient and standard deviation for the MLR with 34 structural parameters were r 2=0.94 and s=0.58 for the training set of 675 compounds. For the test set of 21 compounds, the equivalent statistics were r 2 pred=0.80 and s=0.87, respectively. Neural networks gave a significant improvement using the same set of parameters, and the standard deviations were s=0.52 for the training set and s=0.75 for the test set when an artificial neural network with five neurons in the hidden layer was used. The results clearly show that accurate models can be rapidly calculated for the estimation of aqueous solubility for a large and diverse set of organic compounds using easily calculated structural parameters.

Zur physikalisch-chemischen Charakterisierung von 5-Amino-1-aryl-1H-tetrazolen: Charakterisierung von Wasserlöslichkeiten und ihre Beziehungen zu Octan-1-ol/Wasser-Verteilungskoeffizienten

The aqueous solubilities (logS) of a set of 27 5-amino-1-aryl-1H-tetrazoles with known biological activity were determined photometrically and correlated with their octan-1-ol/water partition coefficients (logP). The otained linear relationship between logS and logP can be significantly improved by a melting point term, an electronic term and several indicator variables describing the deviating solubility behaviour of compounds with hydrogen bonding groups and the dependence on substituent position. The calculated moreparameter equations allow the reliable prediction of aqueous solubilities for 5-amino-1-aryl-1H-tetrazoles not yet synthesized or investigated.

Prediction of Aqueous Solubility of Organic Compounds from Molecular Structure

Multiple linear regression (MLR) and computational neural networks (CNN) are utilized to develop mathematical models to relate the structures of a diverse set of 332 organic compounds to their aqueous solubilities. Topological, geometric, and electronic descriptors are used to numerically represent structural features of the data set compounds. Genetic algorithm and simulated annealing routines, in conjunction with MLR and CNN, are used to select subsets of descriptors that accurately relate to aqueous solubility. Nonlinear models with nine calculated structural descriptors are developed that have a training set root-mean-square error of 0.394 log units for compounds which span a −log(molarity) range from −2 to +12 log units.

Aqueous solubility prediction of environmentally important chemicals from the mobile order thermodynamics

Applied to the aqueous solubility of only-sligthly polar compounds with no hydrogen bond donor capacity, the quantitative development of the thermodynamics of mobile order results in a very simple nearly linear predictive solubility equation requiring only the molar volume of the solute and its melting properties in the case of solids. Enhancement of the solubility by weak solvation effects associated to the proton-acceptor ability of the solute towards water is taken into account through standard stability constants reflecting the functionality, the degree of unsaturation and cyclization of the molecule. Accordingly, the molar aqueous solubility of a diverse set of 531 chemicals of environmental relevance is predicted with an average absolute error of 0.37 log units though the solubilities span over more than 12 orders of magnitude. The analysis of the relative importance of the terms contributing to the solubility demonstrates that the poor solubility of low-polarity compounds in water is merely the result of the hydrophobic effect and of its extremely rapid increase with the size of the solute. The mobile order thermodynamics also provides explanations for the empirically deduced solubility-volume linear relationships.

UNIFAC Prediction of Aqueous and Nonaqueous Solubilities of Chemicals with Environmental Interest

The aqueous and nonaqueous solubilities of a vast number of chemicals with significant environmental roles have been predicted using the latest version of UNIFAC group interaction parameters. A few critical measurements to test specific UNIFAC calculations of nonaqueous solubilities are also reported. The chemicals included in the calculation have aqueous solubilities that span 11 orders of magnitude. Good agreement was observed between the UNIFAC-predicted and literature-reported aqueous solubilities for 11 groups of compounds, i.e., short-chain alkanes, alkenes, alcohols, chlorinated alkanes, alkyl benzenes, chlorinated benzenes, PAHs, PCBs, anilines, phenols, and organohalide insecticides (DDT and lindane). Similarly, UNIFAC successfully predicts the co-solvency of PCB in methanol/water solutions. The error between predicted and literature-reported aqueous solubilities was larger for three groups of chemicals: long-chain alkanes, phthalates, and chlorinated alkenes. The average absolute error in UNIFA...

ChemInform Abstract: Linear Solvation Energy Relationships: An Improved Equation for Correlation and Prediction of Aqueous Solubilities of Aromatic Solutes Including Polycyclic Aromatic Hydrocarbons and Polychlorinated Biphenyls.

AbstractChemInform is a weekly Abstracting Service, delivering concise information at a glance that was extracted from about 100 leading journals. To access a ChemInform Abstract of an article which was published elsewhere, please select a “Full Text” option. The original article is trackable via the “References” option.

Prediction of Aqueous Solubility of Organic Compounds

Prediction of Aqueous Solubility of Organic Compounds

Prediction of aqueous solubility and octanol—water partition coefficient for pesticides based on their molecular structure

Correlations have been derived for the estimation of aqueous solubilities and octanol—water partition coefficients for pesticides, based solely on their molecular structure. The parameters used for obtaining these correlations are the zero order connectivity, zero order valence molecular connectivity, molecular polarizability, and carbon to hydrogen ratio. The results obtained are satisfactory for environmental appli- cations. Such correlations may be used to predict the logarithm of aqueous solubilities (<0.1) and/or the logarithm of the octanol—water partition coefficients (> −0.4) for pesticides. However, these correlations cannot be used satisfactorily for pesticides having a carbon to hydrogen ratio >/ 2, or O-analogues.

Estimation of the aqueous solubility of complex organic compounds

The aqueous solubility of organic compounds depends on two contributing parameters: the ideal solubility and the activity coefficient. The methods available for the prediction of aqueous solubility have the ideal solubility term in common. They differ in the way they account for the activity coefficient. Various empirical and theoretical methods for predicting activity coefficients are compared in this report. The methods were first used with a Training Set of solutes. The same methods were then applied to a Test Set of solutes, which consisted of a series of compounds of pharmaceutical and environmental relevance. The Test Set encompassed a wide variety of chemical structures and high degree of structural complexity. Only those methods with a sound theoretical rationale are useful for both the Training and Test sets. A comparative evaluation of the methods tested was made through the quantification of the accuracy of their predictions and their range of applicability.

Application of the mobile order theory to the prediction of aqueous solubility of chlorinated benzenes and biphenyls

Prediction of the aqueous solubility of solid and liquid polychlorinated biphenyls and benzenes is obtained by means of the mobile order theory. The solubility values are mainly determined by the magnitude of the hydrophobic effect. This effect does not result from the breaking of solvent H-bonded chains in order to separate the solvent molecules to provide a suitably sized cavity for the solute, but from a decrease of the mobile order entropy of each water molecule when the hydrogen-bonded molecules are brought in a large volume consecutively to the addition of the solute. The evolution of the solubility with the chlorine substitution is further explained.

Correlation of aqueous solubility and octanol-water partition coefficient based on molecular structure

Correlations have been proposed for prediction of aqueous solubility and octanol-water partition coefficients for halogenated benzenes, polycyclic aromatic hydrocarbons and polychlorinated biphenyls from first order molecular connectivity. Molecular connectivity can be easily calculated solely from molecular structure of the compound. Correlation contains only one variable and predicts aqueous solubility for these three groups of compounds with reasonable accuracy extending over nine orders of magnitude. The observed percent standard deviations in log aqueous solubility and log partition coefficients are 9.4 and 4.2 respectively. The correlation with a correction term for solids did not offer any significant advantage over the situation without a correction term.

Comment on "Prediction of aqueous solubility of organic chemicals based on molecular structure. 2. Application to PNAs, PCBs, PCDDs, etc."

Comment on "Prediction of aqueous solubility of organic chemicals based on molecular structure. 2. Application to PNAs, PCBs, PCDDs, etc."

Prediction of aqueous solubility and the octanol-water partition coefficient for lipophilic organic compounds using molecular descriptors and physicochemical properties

The utility of thirty-nine molecular descriptors and physicochemical properties to model the solubility (S) and octanol-water partition coefficient (Kow) of thirty-one lipophilic organic compounds was assessed using least squares linear regression analysis. The modelling of Kow when all the compounds were treated together or in groups of structurally related compounds was adequate however, utility for prediction was greatly improved by separate treatment of ionizing and non-ionizing compounds. The log Kow values of the ionizing compounds could best be predicted by multi-parametric linear regression (MLR) equations utilising pKa and molecular weight. The linear regression equations for log S were not as significant as those for log Kow. Several novel molecular descriptors that yielded high correlation coefficients in linear regression equations were the approximate sigma electron density, radius of gyration and the first and second principle moments of inertia. Other useful properties were the sum of all absolute valency charges, density at 20°C, liquid density and pKa.