Quantitative Structure−Property Relationships (QSPRs) for the Estimation of Vapor Pressure:  A Hierarchical Approach Using Mathematical Structural Descriptors

Abstract

A set of 379 molecular descriptors was calculated for use in hierarchical quantitative structure-property relationship (QSPR) modeling of vapor pressure for a structurally diverse database consisting of 469 chemicals. The hierarchical approach utilizes topostructural, topochemical, geometrical, and quantum chemical descriptors in a stepwise fashion to develop QSPR models. In this way, the relative roles of the various levels of descriptors can be examined. The results show that the easily calculated topological descriptors explain the majority of the variance and that the addition of geometrical and quantum chemical descriptors does not result in a significantly improved model.

Prediction of physicochemical properties of energetic materials

“…Physical and transport properties examined include melting point and density [1], as well as viscosity, surface tension and thermal conductivity [2]. Physicochemical properties examined include the octanol-water partition coefficient [3,4], aqueous solubility [5][6][7] soil-sorption coefficient [8], vapor pressure [9], and Henry's law constant [10]. Two primary approaches have been taken in the prediction of physicochemical parameters based on the structure of a material: (1) group contribution methods and (2) quantitative structure-property/activity relationships (QSPR/QSAR).…”

Development of quantitative structure-activity relationship models for vapor pressure estimation using computed molecular descriptors

“…Basak et al formulated the hierarchical quantitative structure-activity relationship (HiQSAR) approach for the estimation of properties, biomedicinal activities, and toxicities of chemicals from computed descriptors. [6][7][8][9][10][11][12][13][14][15][16][17][18] The objective of this HiQSAR/ HiQSPR research has been twofold: description and prediction. The HiQSPR formalism uses progressively more complex indices in the development of models.…”

“…Basak et al have used the HiQSPR approach previously in the development of VP prediction models. 12,15 However, the current study utilizes an expanded set of descriptors along with three statistical modeling approaches, namely ridge regression (RR), principal components regression (PCR), and partial least squares (PLS) regression, which are appropriate for data sets wherein the number descriptors is large with respect to the number of chemical compounds and when the molecular descriptors are highly intercorrelated.…”

Similarity methods in analog selection, property estimation and clustering of diverse chemicals

“…This complex fuel mixture has been shown to cause skin irritation, immunosuppression, and systemic toxicity. 95 If we were to test all possible mixtures of known JP-8 chemicals, then we would have to test 228! mixtures.…”