Molecular Modeling by Linear Combinations of Connectivity Indexes

Abstract

The modeling power of the method of linear combinations of connectivity indexes (LCCI), based on a minimal and on an expanded set of connectivity indexes, has been tested on several properties of different classes of organic compounds: the melting points and motor octane numbers of alkanes, the melting points and solubilities of caffein homologues, and four different physicochemical properties of organophosphorus compounds. The modeling of the first property, a classical shape-dependent property and up to date a challenging problem of molecular modeling, was resolved by partitioning the entire set of alkanes into congruent subsets. A minimal set of normal and valence connectivity indexes was able to model the melting points of caffein homologues that have quite similar molecular shapes and sizes, while the modeling of the solubilities of these homologues was unravelled by taking into consideration their association in solution and by employing linear combinations of squared connectivity indexes. The very effective modeling of the two different types (shape-and sizedependent) of properties of the organophosphorus compounds, with a minimal set of connectivity indexes, delineates also a test for the proposed valence 6" value of phosphorus in organophosphorus derivatives. Linear LCOCI combinations of orthogonal connectivity indexes were also tested to improve, if possible, the modeling of the properties of the given classes of compounds. Modeled properties show that the connectivity indexes can be highly dependent on the detailed knowledge of the physicochemical state of the investigated system and that, usually, LCCIs with a minimal basis set yield quite adequate modeling.

Modeling the Aqueous Solubility of Aliphatic Alcohols

“…where r = the correlation coefficient, s = the standard error of estimate, F = the F-test and Q is Pogliani's quality factor given by the ratio rjs [22].…”

Degree-Based Topological Indices

“…18,19,[170][171][172][173][174][175][176][177][178][179] In contrast to this, not many mathematical results have been obtained. 20,[180][181][182][183] If the exponent λ in Equation (13) is chosen to be different than -0.5, then we arrive at an infinite class of topological indices of the form…”

Quantitative structure‐property relationships for prediction of boiling point, vapor pressure, and melting point

“…Cherqaoui et al [131] used a neural network approach to predict the melting points of all 150 alkanes up to C 10 ; they obtained r 2 = 0.956 and s = 8.1°. Pogliani [128–130] used modified connectivity indices with a small series of alkanes, and Todeschini and Gramatica [47] and Todeschini et al [133] applied their new WHIM descriptors to model melting points of polyaromatic hydrocarbons.…”

“…Cherqaoui et al[131] used a neural network approach to predict the melting points of all 150 alkanes up to C 10 ; they obtained r 2 ϭ 0.956 and s ϭ 8.1Њ. Pogliani[128][129][130] used modified connectivity indices with a small series of alkanes, and Todeschini and Gramatica[47] and Todeschini et al[133] applied their new WHIM descriptors to model melting points of polyaromatic hydrocarbons.Within congeneric series (i.e., a common core with diverse substituents), Dearden and Rahman[152] obtained a standard error of 20.5Њ for a series of 41 substituted anilines. Katritzky and Gordeeva[139] found reasonable correlations (r 2 Ն 0.795) for series of 27 aldehydes, 48 amines, and 30 ketones separately (s not given).…”