: The acid dissociation constant p Ka is a very important molecular property, and there is a strong interest in the development of reliable and fast methods for p Ka prediction. We have evaluated the p Ka prediction capabilities of QSPR models based on empirical atomic charges calculated by the Electronegativity Equalization Method (EEM). Specifically, we collected 18 EEM parameter sets created...

MOLGEN-QSPR is a software newly developed for use in quantitative structure property relationships (QSPR) work. It allows to import, to manually edit, or to generate chemical structures, to detect duplicate structures, to import or to manually input property values, to calculate the values of a broad pool of molecular descriptors, to establish QSPR equations (models), and using such models to p...

In this chapter, the application of computational techniques in environmental exposure assessment was described. The most important groups of these techniques are Multimedia Mass-balance (MM) modelling and Quantitative Structure-Activity/Structure-Property Relationships (QSAR/QSPR) modelling. Multimedia Mass-balance models have been widely utilized for studying Long-Range Transport Potential (L...

QSPR modelling is a very useful and popular methodology for estimating the physical and chemical properties of molecules. The inputs for QSPR models are 3D structures of molecules. Currently, the 3D structures for millions of molecules are publicly available. A large number of these 3D structures were generated by software tools for the conversion of 2D structures into 3D. Moreover, the generat...

This paper gives an overview of the mathematical methods currently used in quantitative structure-activity/property relationship (QASR/QSPR) studies. Recently, the mathematical methods applied to the regression of QASR/QSPR models are developing very fast, and new methods, such as Gene Expression Programming (GEP), Project Pursuit Regression (PPR) and Local Lazy Regression (LLR) have appeared o...

The prediction of physical and chemical properties of molecules is a very important step in the drug discovery process. QSAR and QSPR models are strong tools for predicting these properties. The models employ descriptors and statistical approaches to provide an estimation of the desired property. An abundance of descriptors and QSAR/QSPR models were published, but the prediction of some propert...

Classical quantitative structure-properties relationship (QSPR) statistical techniques unavoidably present an inherent paradoxical computational context. They rely on the definition of a Gram matrix in descriptor spaces, which is used afterwards to reduce the original dimension via several possible kinds of algebraic manipulations. From there, effective models for the computation of unknown pro...

Quantitative structure-property relationship (QSPR) analysis has been directed to a series of pure nonionic surfactants containing linear alkyl, cyclic alkyl, and alkey phenyl ethoxylates. Modeling of cloud point of these compounds as a function of the theoretically derived descriptors was established by multiple linear regression (MLR) and partial least squares (PLS) regression. In this study,...

Development of Quantitative Structure Property Relationships (QSPR) for property prediction, targeted for a particular applicability domain (AD), and definition of the AD boundaries are considered. The AD is defined in terms of the target compound (for which a property has to be predicted) belonging to a homologous series and including carbon atoms above a particular number. If the target compo...

This paper presents a review on recent progress in quantitative structure-property relationship (QSPR) studies of surfactants and applications of various molecular descriptors. QSPR studies on critical micelle concentration (cmc) and surface tension (gamma) of surfactants are introduced. Studies on charge distribution in ionic surfactants by quantum chemical calculations and its effects on the ...