Prediction Of Aqueous Solubility Research Articles

Recent progress in the computational prediction of aqueous solubility and absorption

The computational prediction of aqueous solubility and/or human absorption has been the goal of many researchers in recent years. Such an in silico counterpart to the biopharmaceutical classification system (BCS) would have great utility. This review focuses on recent developments in the computational prediction of aqueous solubility, P-glycoprotein transport, and passive absorption. We find that, while great progress has been achieved, models that can reliably affect chemistry and development are still lacking. We briefly discuss aspects of emerging scientific understanding that may lead to breakthroughs in the computational modeling of these properties.

In Silico Approaches to Prediction of Aqueous and DMSO Solubility of Drug-Like Compounds: Trends, Problems and Solutions

The solubility of drugs and drug-like compounds has been the subject of extensive studies aimed at finding a way to predict solubility from molecular structure. The aqueous solubility of a drug is an important factor that influences its absorption, distribution and elimination in the body. Poor aqueous solubility often causes a drug to appear inactive and may cause other biological problems. Compound solubility in DMSO represents another serious problem in early stages of drug discovery. An appreciation of the factors affecting a compound's DMSO solubility could help in predicting the storage conditions and appropriateness of compounds for primary bioscreening programs. In silico procedures for estimation of water and DMSO solubility represent extremely useful tools for the drug discovery practitioners. In this review, we provide a critical discussion of in silico models for the prediction of DMSO and water solubility of drug-like compounds used for virtual screening. We describe the main tendencies in the field, "booming" approaches and unsolved problems. A critical analysis of the accuracy and applicability of methods is provided.

Recent progress in the computational prediction of aqueous solubility and absorption

Recent progress in the computational prediction of aqueous solubility and absorption

Global and Local Computational Models for Aqueous Solubility Prediction of Drug‐Like Molecules.

ChemInformVolume 35, Issue 39 Other Subjects Global and Local Computational Models for Aqueous Solubility Prediction of Drug-Like Molecules. Christel A. S. Bergstroem, Christel A. S. Bergstroem Dep. Chem., Univ. Goeteborg, S-412 96 Goeteborg, Swed.Search for more papers by this authorCarola M. Wassvik, Carola M. Wassvik Dep. Chem., Univ. Goeteborg, S-412 96 Goeteborg, Swed.Search for more papers by this authorUlf Norinder, Ulf Norinder Dep. Chem., Univ. Goeteborg, S-412 96 Goeteborg, Swed.Search for more papers by this authorKristina Luthman, Kristina Luthman Dep. Chem., Univ. Goeteborg, S-412 96 Goeteborg, Swed.Search for more papers by this authorPer Artusson, Per Artusson Dep. Chem., Univ. Goeteborg, S-412 96 Goeteborg, Swed.Search for more papers by this author Christel A. S. Bergstroem, Christel A. S. Bergstroem Dep. Chem., Univ. Goeteborg, S-412 96 Goeteborg, Swed.Search for more papers by this authorCarola M. Wassvik, Carola M. Wassvik Dep. Chem., Univ. Goeteborg, S-412 96 Goeteborg, Swed.Search for more papers by this authorUlf Norinder, Ulf Norinder Dep. Chem., Univ. Goeteborg, S-412 96 Goeteborg, Swed.Search for more papers by this authorKristina Luthman, Kristina Luthman Dep. Chem., Univ. Goeteborg, S-412 96 Goeteborg, Swed.Search for more papers by this authorPer Artusson, Per Artusson Dep. Chem., Univ. Goeteborg, S-412 96 Goeteborg, Swed.Search for more papers by this author First published: 06 September 2004 https://doi.org/10.1002/chin.200439214AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume35, Issue39September 28, 2004 RelatedInformation

QSPR modeling aqueous solubility of polychlorinated biphenyls by optimization of correlation weights of local and global graph invariants

AbstractAqueous solubilities of polychlorinated biphenyls have been correlated with topological molecular descriptors which are functions of local and global invariants of labeled hydrogen filled graphs. Morgan extended connectivity and nearest neighboring codes have been used as local graph invariants. The number of chlorine atoms in biphenyls has been employed as a global graph invariant. Present results show that taking into account correlation weights of global invariants gives quite reasonable improvement of statistical characteristics for the prediction of aqueous solubilities of polychlorinated biphenyls.

Global and local computational models for aqueous solubility prediction of drug-like molecules.

The aim of this study was to develop in silico protocols for the prediction of aqueous drug solubility. For this purpose, high quality solubility data of 85 drug-like compounds covering the total drug-like space as identified with the ChemGPS methodology were used. Two-dimensional molecular descriptors describing electron distribution, lipophilicity, flexibility, and size were calculated by Molconn-Z and Selma. Global minimum energy conformers were obtained by Monte Carlo simulations in MacroModel and three-dimensional descriptors of molecular surface area properties were calculated by Marea. PLS models were obtained by use of training and test sets. Both a global drug solubility model (R(2) = 0.80, RMSE(te) = 0.83) and subset specific models (after dividing the 85 compounds into acids, bases, ampholytes, and nonproteolytes) were generated. Furthermore, the final models were successful in predicting the solubility values of external test sets taken from the literature. The results showed that homologous series and subsets can be predicted with high accuracy from easily comprehensible models, whereas consensus modeling might be needed to predict the aqueous drug solubility of datasets with large structural diversity.

Prediction of aqueous solubility of organic salts of diclofenac using PLS and molecular modeling

Organic salts of diclofenac were predicted by using computed molecular descriptors and multivariate partial least squares (PLS). The molecular descriptors including binding energy and surface area of salts were calculated by the use of Hyperchem and ChemPlus QSAR programs for Windows. Other physicochemical properties such as hydrogen acceptor for oxygen atoms, hydrogen acceptor for nitrogen atoms, hydrogen bond donors, hydrogen bond-forming ability, molecular weight, and log partition coefficient (log P) of bases were also used as descriptors. Good statistical models were derived that permit simple computational prediction of salt solubility of a same parent structure. The final models derived had R 2 value=0.96 and root mean square error for prediction (RMSEP) values ranging from 0.021 to 0.054 (log scale). Preferably all utilized descriptors in the final models can readily obtain from the chemical structure of salt and base. Molecular weight of base is one of the important factors associated with salt solubility. While increased molecular weight of base, surface area of salt and hydrogen bonding ability of base increase solubility, and increased binding energy and log P of base have negative effect on salt solubility.

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.

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.

Prediction of Aqueous Solubility of a Diverse Set of Compounds Using Quantitative Structure−Property Relationships

"Fail early and fail fast" is the current paradigm that the pharmaceutical industry has adopted widely. Removing non-drug-like compounds from the drug discovery lifecycle in the early stages can lead to tremendous savings of resources. Thus, fast screening methods are needed to profile the large collection of synthesized and virtual libraries involved in the early stage. Solubility is one of the filters that are applied extensively to ensure that the compounds are reasonably soluble so that synthesis of the compounds and assay studies of pharmacokinetics and toxicity are feasible. To address this need, we have developed a fast quantitative structure-property relationship (QSPR) model for the prediction of aqueous solubility (at 298 K, unbuffered solution) from the molecular structures. Multiple linear regressions and genetic algorithms were used to develop the models. The model was based on a set of diverse compounds including small organic molecules and drug and drug-like species. The predicted solubility for the training and test sets agrees well with the experimental values. The coefficient of determination is R(2) = 0.84 for the training set of 775 compounds and the RMS error = 0.87. This model was validated on four sets of compounds. The RMS error for the 1665 compounds from the four validation data sets (including compounds from the Physician's Desk References and Comprehensive Medicinal Chemistry databases) is 1 log unit and the unsigned error is 0.77. This model does not require 3-D structure generation which is rather time-consuming. Using 2-D structure as input, this model is able to compute solubility for 90 000-700 000 compounds/h on a SGI Origin 2000 workstation. This kind of fast calculation allows the model to be used in data mining and screening of large synthesized or virtual libraries.

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.

In silico prediction of aqueous solubility, human plasma protein binding and volume of distribution of compounds from calculated pKa and AlogP98 values.

We have investigated whether three important ADME (absorption, distribution, metabolism, excretion) related properties (aqueous solubility, human plasma protein binding, and human volume of distribution at steady-state) can be predicted from chemical structure alone if only the predicted predominant ionisation state and lipophilicity (calculated logP [P = octanol-water partition coefficient]) are considered. A simple, fast method for the in silico prediction of aqueous solubility of predominantly uncharged compounds has been developed, while some potential is shown for the prediction of predominantly charged or zwitterionic compounds. Ten other known in silico prediction methods for aqueous solubility have also been evaluated. It has furthermore been demonstrated that the molecular weight (MW) profile of training sets for the development of aqueous solubility prediction methods can influence their predictive performance with regard to test sets of either matching or diverging profiles. The same property descriptors which have been found most relevant for the prediction of aqueous solubility have also proved useful for the prediction of human plasma protein binding and human volume of distribution at steady-state.

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.

Thermochemistry and aqueous solubilities of hydrotalcite-like solids.

Hydrotalcites are used in technology as catalysts and anion exchangers and are important sinks for environmental contaminants. Their compositional variability makes it important, but difficult, to estimate their aqueous solubility. We report calorimetric measurements of the heats of formation of cobalt-aluminum hydrotalcite phases. The heat and free energy of formation from the elements are equal to those of mechanical mixtures of binary compounds, namely hydroxides and carbonates. The interlayer anion is much more important than the cation in determining the solubility of the hydrotalcite phase and its ability to contain or release heavy metals to the environment. Because hydrotalcites do not have an unreactive polymer as a structural core, their aqueous stability will change dramatically with composition, particularly with anion content. This simple mechanical mixture model allows prediction of aqueous solubilities and trace metal retention in a variety of geochemical settings.

Applications of artificial neural network in AIDS research and therapy.

In recent years considerable effort has been devoted to applying pattern recognition techniques to the complex task of data analysis in drug research. Artificial neural networks (ANN) methodology is a modeling method with great ability to adapt to a new situation, or control an unknown system, using data acquired in previous experiments. In this paper, a brief history of ANN and the basic concepts behind the computing, the mathematical and algorithmic formulation of each of the techniques, and their developmental background is presented. Based on the abilities of ANNs in pattern recognition and estimation of system outputs from the known inputs, the neural network can be considered as a tool for molecular data analysis and interpretation. Analysis by neural networks improves the classification accuracy, data quantification and reduces the number of analogues necessary for correct classification of biologically active compounds. Conformational analysis and quantifying the components in mixtures using NMR spectra, aqueous solubility prediction and structure-activity correlation are among the reported applications of ANN as a new modeling method. Ranging from drug design and discovery to structure and dosage form design, the potential pharmaceutical applications of the ANN methodology are significant. In the areas of clinical monitoring, utilization of molecular simulation and design of bioactive structures, ANN would make the study of the status of the health and disease possible and brings their predicted chemotherapeutic response closer to reality.

Differential Shannon entropy analysis identifies molecular property descriptors that predict aqueous solubility of synthetic compounds with high accuracy in binary QSAR calculations.

Prediction of aqueous solubility of organic molecules by binary QSAR was used as a test case for a recently introduced entropy-based descriptor selection method. Property descriptors suitable for solubility predictions were exclusively selected on the basis of Shannon entropy calculations in molecular learning sets, not taking any other information into account. Sets of only five or 10 2D descriptors with largest entropy differences between molecules above or below a defined solubility threshold yielded consistently high prediction accuracy between 80% and 90% in binary QSAR calculations, regardless of the threshold values applied. The top five descriptors with largest differential Shannon entropy (DSE) values achieved an average prediction accuracy of 88%. These findings suggest that differences in entropy and relative information content of descriptors in compared compound data sets correlate with significant differences in physical properties and support the practical relevance of entropy-based descriptor selection routines. The study also demonstrates that binary QSAR methodology can be effectively used to classify small molecules according to aqueous solubility.

Prediction of drug solubility from structure

The aqueous solubility of a drug is an important factor affecting its bioavailability. Numerous computational methods have been developed for the prediction of aqueous solubility from a compound’s structure. A review is provided of the methodology and quality of results for the most useful procedures including the model implemented in the QikProp program. Viable methods now exist for predictions with less than 1 log unit uncertainty, which is adequate for prescreening synthetic candidates or design of combinatorial libraries. Further progress with predictive methods would require an experimental database of highly accurate solubilities for a large, diverse collection of drug-like molecules.

Experimental and computational screening models for prediction of aqueous drug solubility.

To devise experimental and computational models to predict aqueous drug solubility. A simple and reliable modification of the shake flask method to a small-scale format was devised, and the intrinsic solubilities of 17 structurally diverse drugs were determined. The experimental solubility data were used to investigate the accuracy of commonly used theoretical and semiexperimental models for prediction of aqueous drug solubility. Computational models for prediction of intrinsic solubility, based on lipophilicity and molecular surface areas, were developed. The intrinsic solubilities ranged from 0.7 ng/mL to 6.0 mg/ mL, covering a range of almost seven log10 units, and the values determined with the new small-scale shake flask method agreed well with published solubility data. Solubility data computed with established theoretical models agreed poorly with the experimentally determined solubilities, but the correlations improved when experimentally determined melting points were included in the models. A new, fast computational model based on lipophilicity and partitioned molecular surface areas, which predicted intrinsic drug solubility with a good accuracy (R2 of 0.91 and RMSEtr of 0.61) was devised. A small-scale shake flask method for determination of intrinsic drug solubility was developed, and a promising alternative computational model for the theoretical prediction of aqueous drug solubility was proposed.

Prediction of aqueous solubility of drugs and pesticides with COSMO-RS.

The COSMO-RS method, originally developed for the prediction of liquid-liquid and liquid-vapor equilibrium constants based on quantum chemical calculations, has been extended to solid compounds by addition of a heuristic expression for the Gibbs free energy of fusion. By this addition, COSMO-RS is now capable of a priori prediction of aqueous solubilities of a wide range of typical neutral drug and pesticide compounds. Only three parameters in the heuristic expression have been fitted on a data set of 150 drug-like compounds. On these data an rms deviation of 0.66 log-units was achieved. Later, the model was tested on a set of 107 pesticides, which have been critically selected based on two experimental data sources and by a crosscheck with an independent HQSAR model. On this data set an rms of 0.61 log-units was achieved, without any adjustments to the structurally extremely diverse pesticides. This result verifies the ability of this extended COSMO-RS to predict aqueous solubilities of drugs and pesticides of almost arbitrary structural classes. The new method is COSMO-RSol.

ChemInform Abstract: Prediction of Aqueous Solubility of Organic Compounds by the General Solubility Equation (GSE).

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.