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 ...

A quantitative structure–property relationship (QSPR) modeling of vapor pressure at 298.15 K, expressed as log (VP / Pa) was performed for a series of 84 hydrocarbons (63 alkanes and 21 cycloalkanes) using the van der Waals (vdW) surface area, SW / Å, calculated by the Monte Carlo method, as the molecular descriptor. The QSPR model developed from the subset of 63 alkanes (C1–C16), deemed as the...

A novel QSPR development technique is proposed with the aim to combine the advantages of the two methods most frequently applied. A quantitative structure-property relationship (QSPR) is developed using this technique to relate the molecular structures of 645 diverse organic compounds to their vapor pressures at 25 C expressed as logVP. The compounds are encoded with topological, electronic, ge...

In this paper we report an attempt to apply the QSPR approach for the analysis of data for mixtures. This is an extension of the conventional QSPR approach to the analysis of data for single molecules. The QSPR methodology was applied to a data set of experimental measured density of binary liquid mixtures compiled from the literature. The present study is aimed to develop models to predict the...

Nowadays, a large amount of experimental and predicted data about the 3D structure of organic molecules and biomolecules is available. Advanced computational methods and high performance computers allow us to obtain large sets of descriptors that can be used to estimate physicochemical properties. It is often of interest to study the correlations between descriptors and properties using multili...

Topological indices are the real number of a molecular structure obtained via molecular graph G. Topological indices are used for QSPR, QSAR and structural design in chemistry, nanotechnology, and pharmacology. Moreover, physicochemical properties such as the boiling point, the enthalpy of vaporization, and stability can be estimated by QSAR/QSPR models. In this study, the QSPR (Quantitative St...

We discuss some features of the orthogonalization methods commonly applied to QSPR QSAR studies. We outline the well known multivariable linear regression analysis in vector form in order to compare mainly Randic and Gram-Schmidt orthogonalization procedures and also cast the basis for other approaches like Löwdin’s one. We expect that present review may become the starting point for future dev...