Aqueous Solubility Of Organic Compounds Research Articles

Aqueous Solubility of Organic Compounds for Flow Battery Applications: Symmetry and Counter Ion Design to Avoid Low-Solubility Polymorphs.

Flow batteries can play an important role as energy storage media in future electricity grids. Organic compounds, based on abundant elements, are appealing alternatives as redox couples for redox flow batteries. The straightforward scalability, the independence of material sources, and the potentially attractive price motivate researchers to investigate this technological area. Four different benzyl-morpholino hydroquinone derivatives were synthesized as potential redox active species. Compounds bearing central symmetry were shown to be about an order of magnitude less soluble in water than isomers without central symmetry. Counter ions also affected solubility. Perchlorate, chlorate, sulfate and phosphate anions were investigated as counter ions. The formations of different polymorphs was observed, showing that their solubility is not a function of their structure. The kinetics of the transformation can give misleading solubility values according to Ostwald’s rule. The unpredictability of both the kinetics and the thermodynamics of the formation of polymorphs is a danger for new organic compounds designed for flow battery applications.

Pushing the limits of solubility prediction via quality-oriented data selection.

SummaryAccurate prediction of the solubility of chemical substances in solvents remains a challenge. The sparsity of high-quality solubility data is recognized as the biggest hurdle in the development of robust data-driven methods for practical use. Nonetheless, the effects of the quality and quantity of data on aqueous solubility predictions have not yet been scrutinized. In this study, the roles of the size and the quality of data sets on the performances of the solubility prediction models are unraveled, and the concepts of actual and observed performances are introduced. In an effort to curtail the gap between actual and observed performances, a quality-oriented data selection method, which evaluates the quality of data and extracts the most accurate part of it through statistical validation, is designed. Applying this method on the largest publicly available solubility database and using a consensus machine learning approach, a top-performing solubility prediction model is achieved.

Predictive models of aqueous solubility of organic compounds built on A large dataset of high integrity

Aqueous solubility is one of the most important properties in drug discovery, as it has profound impact on various drug properties, including biological activity, pharmacokinetics (PK), toxicity, and in vivo efficacy. Both kinetic and thermodynamic solubilities are determined during different stages of drug discovery and development. Since kinetic solubility is more relevant in preclinical drug discovery research, especially during the structure optimization process, we have developed predictive models for kinetic solubility with in-house data generated from 11,780 compounds collected from over 200 NCATS intramural research projects. This represents one of the largest kinetic solubility datasets of high quality and integrity. Based on the customized atom type descriptors, the support vector classification (SVC) models were trained on 80% of the whole dataset, and exhibited high predictive performance for estimating the solubility of the remaining 20% compounds within the test set. The values of the area under the receiver operating characteristic curve (AUC-ROC) for the compounds in the test sets reached 0.93 and 0.91, when the threshold for insoluble compounds was set to 10 and 50 μg/mL respectively. The predictive models of aqueous solubility can be used to identify insoluble compounds in drug discovery pipeline, provide design ideas for improving solubility by analyzing the atom types associated with poor solubility and prioritize compound libraries to be purchased or synthesized.

Measurement and Modeling of Setschenow Constants for Selected Hydrophilic Compounds in NaCl and CaCl2 Simulated Carbon Storage Brines.

Carbon capture, utilization, and storage (CCUS), a climate change mitigation strategy, along with unconventional oil and gas extraction, generates enormous volumes of produced water containing high salt concentrations and a litany of organic compounds. Understanding the aqueous solubility of organic compounds related to these operations is important for water treatment and reuse alternatives, as well as risk assessment purposes. The well-established Setschenow equation can be used to determine the effect of salts on aqueous solubility. However, there is a lack of reported Setschenow constants, especially for polar organic compounds. In this study, the Setschenow constants for selected hydrophilic organic compounds were experimentally determined, and linear free energy models for predicting the Setschenow constant of organic chemicals in concentrated brines were developed. Solid phase microextraction was employed to measure the salting-out behavior of six selected hydrophilic compounds up to 5 M NaCl and 2 M CaCl2 and in Na-Ca-Cl brines. All compounds, which include phenol, p-cresol, hydroquinone, pyrrole, hexanoic acid, and 9-hydroxyfluorene, exhibited log-linear behavior up to these concentrations, meaning Setschenow constants previously measured at low salt concentrations can be extrapolated up to high salt concentrations for hydrophilic compounds. Setschenow constants measured in NaCl and CaCl2 brines are additive for the compounds measured here; meaning Setschenow constants measured in single salt solutions can be used in multiple salt solutions. The hydrophilic compounds in this study were selected to elucidate differences in salting-out behavior based on their chemical structure. Using data from this study, as well as literature data, linear free energy relationships (LFERs) for prediction of NaCl, CaCl2, LiCl, and NaBr Setschenow constants were developed and validated. Two LFERs were improved. One LFER uses the Abraham solvation parameters, which include the index of refraction of the organic compound, organic compound's polarizability, hydrogen bonding acidity and basicity of the organic compound, and the molar volume of the compound. The other uses an octanol-water partitioning coefficient to predict NaCl Setschenow constants. Improved models from this study now include organic compounds that are structurally and chemically more diverse than the previous models. The CaCl2, LiCl, and NaBr single parameter LFERs use concepts from the Hofmeister series to predict new, respective Setschenow constants from NaCl Setschenow constants. The Setschenow constants determined here, as well as the LFERs developed, can be incorporated into CCUS reactive transport models to predict aqueous solubility and partitioning coefficients of organic compounds. This work also has implications for beneficial reuse of water from CCUS; this can aide in determining treatment technologies for produced waters.

Comments on prediction of the aqueous solubility using the general solubility equation (GSE) versus a genetic algorithm and a support vector machine model

The general solubility equation (GSE) is the state-of-the-art method for estimating the aqueous solubilities of organic compounds. It is an extremely simple equation that expresses aqueous solubility as a function of only two inputs: the octanol–water partition coefficient calculated by readily available softwares like clogP and ACD/logP, and the commonly known melting point of the solute. Recently, Bahadori et al. proposed that their genetic algorithm support vector machine is a “better” predictor. This paper compares the use of the of Bahadori et al. model for the prediction of aqueous solubility to the existing GSE model.

Better prediction of aqueous solubility of chlorinated hydrocarbons using support vector machine modeling

Remediation of water contaminated by organic pollutants is a major challenge, which could be improved by better knowledge on the aqueous solubility of organic compounds. Indeed, the aqueous solubility controls the fate and toxicity of pollutants. Here we performed a structure–property study based on a genetic algorithm for the prediction of aqueous solubility of chlorinated hydrocarbons. 1497 descriptors were calculated with the Dragon software. The variable selection method of the genetic algorithm was used to select an optimal subset of descriptors that have significant contribution to the overall aqueous solubility, from the large pool of calculated descriptors. The support vector machine was then employed to model the possible quantitative relationships between selected descriptors and aqueous solubility. Our results show that total size, polarizability and electronegativity modify the aqueous solubility of compounds. We also found that the support vector machine method gave better results than other methods such as principal component regression and partial least squares.

Measurement of Setschenow constants for six hydrophobic compounds in simulated brines and use in predictive modeling for oil and gas systems.

Treatment and reuse of brines, produced from energy extraction activities, requires aqueous solubility data for organic compounds in saline solutions. The presence of salts decreases the aqueous solubility of organic compounds (i.e. salting-out effect) and can be modeled using the Setschenow Equation, the validity of which has not been assessed in high salt concentrations. In this study, we used solid-phase microextraction to determine Setschenow constants for selected organic compounds in aqueous solutions up to 2-5 M NaCl, 1.5-2 M CaCl2, and in Na-Ca binary electrolyte solutions to assess additivity of the constants. These compounds exhibited log-linear behavior up to these high NaCl concentrations. Log-linear decreases in solubility with increasing salt concentration were observed up to 1.5-2 M CaCl2 for all compounds, and added to a sparse database of CaCl2 Setschenow constants. Setschenow constants were additive in binary electrolyte mixtures. New models to predict CaCl2 and KCl Setschenow constants from NaCl Setschenow constants were developed, which successfully predicted the solubility of the compounds measured in this study. Overall, data show that the Setschenow Equation is valid for a wide range of salinity conditions typically found in energy-related technologies.

Solubility of 3-{3,5-Bis(trifluoromethyl)phenyl}quinoline Using Micellar Solutions of Surfactants

Amphiphilic substances, surfactants, are in common use to enhance the aqueous solubility of organic compounds. Solubilization of 3-{3,5-bis (trifluoromethyl)phenyl}quinoline was investigated by employing spectrophotometry, light scattering and tensiometry in micellar solutions of nonionic surfactants {dimethyldodecylamine-N-oxide, DDAO; polyoxyethylene(23)lauryl ether, Brij35; and polyoxyethylene(04)lauryl ether, Brij30}, a cationic surfactant (dodecyltrimethyl-ammonium bromide, DTAB), anionic surfactants (sodium dodecylbenzenesulfonate, SDBS; sodium dodecyl sulfate, SDS), and a zwitterionic surfactant (N-dodecyl-N,N-dimethyl-3-ammonio-1-propanesulfonate, DDAPS). It is noted that the solubility of the title compound 3-{3,5-bis (trifluoromethyl)phenyl}quinoline increased linearly with the increase in surfactant concentration irrespective of the type of surfactant. This is due to association of the title compound with surfactants. The molar solubilization ratio (MSR), micelle–water partition coefficient (KM), and the Gibbs energy of solubilization (\( \Delta G_{\text{S}}^{\text{o}} \)) of the title compound in the micelles were determined and correlated with each other. The nonionic surfactant DDAO showed the highest solubilization capacity. The order of degree of solubility among nonionic, cationic, and anionic surfactants for 3-{3,5-bis (trifluoromethyl)phenyl}quinoline is DDAO > DDAPS > SDBS > DTAB > SDS > Brij35 > Brij30. This comparative study can be used to select an appropriate medium for the title compound’s solubilization, where the nonionic surfactants DDAO, Brij35 and Brij30 would be more beneficial due to their minimal protein binding and retention of their micellar form even after large dilution in blood owing to their very low critical micellar concentration values. The light scattering study indicated that the hydrodynamic radius of the blank and drug-loaded surfactants remained the same within experimental error.

Capturing the Crystal: Prediction of Enthalpy of Sublimation, Crystal Lattice Energy, and Melting Points of Organic Compounds

Accurate computational prediction of melting points and aqueous solubilities of organic compounds would be very useful but is notoriously difficult. Predicting the lattice energies of compounds is key to understanding and predicting their melting behavior and ultimately their solubility behavior. We report robust, predictive, quantitative structure-property relationship (QSPR) models for enthalpies of sublimation, crystal lattice energies, and melting points for a very large and structurally diverse set of small organic compounds. Sparse Bayesian feature selection and machine learning methods were employed to select the most relevant molecular descriptors for the model and to generate parsimonious quantitative models. The final enthalpy of sublimation model is a four-parameter multilinear equation that has an r(2) value of 0.96 and an average absolute error of 7.9 ± 0.3 kJ.mol(-1). The melting point model can predict this property with a standard error of 45° ± 1 K and r(2) value of 0.79. Given the size and diversity of the training data, these conceptually transparent and accurate models can be used to predict sublimation enthalpy, lattice energy, and melting points of organic compounds in general.

Thermodynamic Modeling of the Aqueous Solubility of PAHs

Polycyclic aromatic hydrocarbons (PAHs) are a family of compounds characterized by having two or more condensed aromatic rings. They are important environmental contaminants associated with oil spills and the incomplete combustion of organic materials. Different models are available in the literature for estimating the aqueous solubilities of organic compounds, but the most accurate are frequently based on the correlation of experimental data, which hampers their use as predictive tools. In this work, the cubic-plus-association equation of state (CPA EoS) is shown to be an accurate model for describing the aqueous solubilities of several solid PAHs in wide temperature and pressure ranges. The results obtained are in very close agreement with the literature data, even when the model is used in a totally predictive scheme. Following previous results on the aqueous solubilities of other organic pollutants, the use of the CPA EoS for describing the aqueous solubilities of organic pollutants such as polycyclic...

Scores of Extended Connectivity Fingerprint as Descriptors in QSPR Study of Melting Point and Aqueous Solubility

QSPR studies, using scores of SciTegic's extended connectivity fingerprint as raw descriptors, were extended to the prediction of melting points and aqueous solubility of organic compounds. Robust partial least-squares models were developed that perform as well as the best published QSPR models for structurally diverse organic compounds. Satisfactory performance of the QSPR models indicates that the scores of extended connectivity fingerprint are high performance molecular descriptors for QSAR/QSPR studies. Performance of the fingerprint-based descriptors is further validated by the satisfactory prediction of aqueous solubility of nearly 1300 organic compounds (squared correlation coefficient of 0.83 and RMSE of 0.85 log unit) with Yalkowsky's general solubility equation using both calculated melting points and calculated octanol-water partition coefficients. It demonstrates for the first time that it is feasible to predict aqueous solubility of structurally diverse organic compounds with the general solubility equation using both the calculated melting points and the partition coefficients.

Thermodynamics’ of dissolution for 1-naphtol N-methylcarbamate in pure, mineral and Mediterranean sea water by dynamic saturation method

This paper mainly concerns the thermodynamic properties of dissolution for 1-naphtol N-methylcarbamate (or carbaryl), which will lead to the Heidman's thermodynamic equation application for this molecule dissolution in pure, mineral and Mediterranean sea water. In fact, the prediction of the transport and fate of hydrophobic organic chemicals in the environment requires the knowledge of their physical and chemical properties, the most important of which is their aqueous solubility. The aim of this work is to set the dynamic saturation method that enables us to measure the aqueous solubilities of organic compounds. Those suffer from lack of data due to solubility below 10 −3 in mole fraction. The carbaryl aqueous solubility study, over a range of temperature between 273.15 K and 318.15 K, was carried out in three different aqueous solvents: pure, mineral, and Mediterranean sea water. An analytical procedure was set by means of GC–FID and optimized in order to quantify the carbaryl by an internal calibration method. The carbaryl showed an aqueous solubility ranging from 3 × 10 −6 to 3.5 × 10 −5 in mole fraction. A salting out effect was well observed and indicated in the solubility data obtained. The solubility values acquired form this method were then used to calculate thermodynamic properties such as Gibbs free energy (Δ sol G°), molar enthalpy of dissolution (Δ sol H°), molar entropy of dissolution (Δ sol S°).

Calculation of Aqueous Solubility of Organic Compounds from Molecular Structural Properties

The quantitative structure-activity relationships (QSAR) correlations were developed for the aqueous solubility of organic compounds at 298.15, 473.15, and 573.15 K. To elucidate the molecular structural variables to control the solvation phenomena, we firstly developed the QSAR correlation of solubility of 108 organic molecules in water at 298.15 K with the use of experimentally and theoretically derivable variables. The experimental variable used was the octanol-water partition coefficient and melting point, while the theoretical variables were LUMO energy and total molecular surface area, which were determined with the COSMO-PM3 method. Based on the results at 298.15 K, the QSAR correlations for aqueous solubility at 473.15 K and 573.15 K were determined with the use of the literature solubility data for 21 compounds at these two high temperatures. It was found that the QSAR correlation with COSMO-PM3 method is able to predict the aqueous solubility of organic compounds at 473.15 and 573.15 K from the molecular structural properties and the melting temperature, since the octanol-water partition coefficient can be predicted from atom/fragment contribution method.

Linear and Nonlinear Methods in Modeling the Aqueous Solubility of Organic Compounds.

Linear and Nonlinear Methods in Modeling the Aqueous Solubility of Organic Compounds.

Linear and Nonlinear Methods in Modeling the Aqueous Solubility of Organic Compounds

Solubility data for 930 diverse compounds have been analyzed using linear Partial Least Square (PLS) and nonlinear PLS methods, Continuum Regression (CR), and Neural Networks (NN). 1D and 2D descriptors from MOE package in combination with E-state or ISIS keys have been used. The best model was obtained using linear PLS for a combination between 22 MOE descriptors and 65 ISIS keys. It has a correlation coefficient (r2) of 0.935 and a root-mean-square error (RMSE) of 0.468 log molar solubility (log S(w)). The model validated on a test set of 177 compounds not included in the training set has r2 0.911 and RMSE 0.475 log S(w). The descriptors were ranked according to their importance, and at the top of the list have been found the 22 MOE descriptors. The CR model produced results as good as PLS, and because of the way in which cross-validation has been done it is expected to be a valuable tool in prediction besides PLS model. The statistics obtained using nonlinear methods did not surpass those got with linear ones. The good statistic obtained for linear PLS and CR recommends these models to be used in prediction when it is difficult or impossible to make experimental measurements, for virtual screening, combinatorial library design, and efficient leads optimization.

Linear and nonlinear functions on modeling of aqueous solubility of organic compounds by two structure representation methods.

Several quantitative models for the prediction of aqueous solubility of organic compounds were developed based on a diverse dataset with 2084 compounds by using multi-linear regression analysis and backpropagation neural networks. The compounds were described by two different structure representation methods: (1) with 18 topological descriptors; and (2) with 32 radial distribution function codes representing the 3D structure of a molecule and eight additional descriptors. The dataset was divided into a training and a test set based on Kohonen's self-organizing neural network. Good prediction results were obtained for backpropagation neural network models: with 18 topological descriptors, for the 936 compounds in the test set, a correlation coefficient of 0.92, and a standard deviation of 0.62 were achieved; with 3D descriptors, for the 866 compounds in the test set, a correlation coefficient of 0.90, and a standard deviation of 0.73 were achieved. The models were also tested by using another dataset, and the relationship of the two datasets was examined by Kohonen's self-organizing neural network.

Estimation of the Aqueous Solubility of Organic Compounds Using Molecular Connectivity indices

A correlation for estimation of the aqueous solubility of organic compounds that is based on a training set of 120 chemicals is proposed. The new model proposed is predictive and requires only molecular connectivity indices in the calculations. The calculated results of the new model are comparable to those from the existing general solubility equation (GSE) and the Klopman–Zhu models. The new model was also applied to a testing set of 80 compounds, and the predictions show that the new model is reliable with good predictive accuracy. Because the new model does not require any experimental physicochemical properties in the calculation, it is simple and easy to apply. This work shows again that molecular connectivity indices are useful structural descriptors in quantitative structure–property (QSPR) studies in pharmaceutical research. © 2003 Wiley‐Liss, Inc. and the American Pharmacists Association J Pharm Sci 92:2284–2294, 2003

Prediction of Aqueous Solubility of Organic Compounds by Topological Descriptors

AbstractTwo quantitative models for the prediction of aqueous solubility of 1293 organic compounds were generated by a Multilinear Regression (MLR) analysis, and a Backpropagation (BPG) neural network. The molecules were represented by 18 topological descriptors. The physicochemical relationship between solubility and the descriptors for different individual classes of monofunctional group compounds such as hydrocarbons, ethers, halocarbons, alcohols, aldehydes and ketones, acids, esters, and amines was investigated. The 1293 compounds were divided into a training set of 741 compounds and a test set of 552 compounds based on a Kohonen's self‐organizing neural network map. The models obtained show a good predictive power: for the test set, a correlation coefficient of 0.97 and a standard deviation of 0.52 were achieved by the backpropagation neural network approch.

Support vector machines for the estimation of aqueous solubility.

Support Vector Machines (SVMs) are used to estimate aqueous solubility of organic compounds. A SVM equipped with a Tanimoto similarity kernel estimates solubility with accuracy comparable to results from other reported methods where the same data sets have been studied. Complete cross-validation on a diverse data set resulted in a root-mean-squared error = 0.62 and R(2) = 0.88. The data input to the machine is in the form of molecular fingerprints. No physical parameters are explicitly involved in calculations.

Prediction of Aqueous Solubility of Organic Compounds Based on a 3D Structure Representation.

Two quantitative models for the prediction of aqueous solubility of 1293 organic compounds were developed by a Multilinear Regression (MLR) analysis and a Back-Propagation (BPG) neural network. The molecules were described by a set of 32 values of a Radial Distribution Function (RDF) code representing the 3D structure and eight additional descriptors. The 1293 compounds were divided into a training set of 797 compounds and a test set of 496 compounds based on a Kohonen self-organizing neural network map. The obtained models show a good predictive power: for the test set, a correlation coefficient of 0.96 and a standard deviation of 0.59 were achieved by the back-propagation neural network approach.