Molecular descriptors atomic partial charges

In the present work, we will use a relatively low level of theory to derive 32 weakly correlated molecular descriptors, each based on the subdivision and classification of the molecular surface area according to three fundamental properties contribution to ClogP, molar refractivity, and atomic partial charge. The resulting collection will be shown to have applicability in QSAR, QSPR, and compound classification. Moreover, the derived 32 descriptors linearly encode most of the information of a collection of traditional mathematical descriptors used in QSAR and QSPR. 

We have derived three sets of (easily calculated) molecular descriptors based on atomic contributions to logP, molar refractivity, and atomic partial charge. The individual descriptors were found to be weakly correlated with each other... 

A set of thirty different descriptors [Stanton and Jurs, 1990] which combine shape and electronic information to characterize molecules and therefore encode features responsible for polar interactions between molecules. The molecule representation used for deriving CPSA descriptors views molecule atoms as hard spheres defined by the - van der Waals radius. The - solvent-accessible surface area SASA is used as the molecular surface area it is calculated using a sphere with a radius of 1.5 A to approximate the contact surface formed when a water molecule interacts with the considered molecule. Moreover, the contact surface where polar interactions can take place is characterized by a specific electronic distribution obtained by mapping atomic partial charges on the solvent-accessible surface. 

Three sets of molecular descriptors that can be computed from a molecular connection table are defined. The descriptors are based on the subdivision and classification of the molecular surface area according to atomic properties (such as contribution to logP, molar refractivity, and partial charge). The resulting 32 descriptors are shown (a) to be weakly correlated with each other (b) to encode many traditional molecular descriptors and (c) to be useful for QSAR, QSPAR, and compound classification. 

Spatial autocorrelation is a quantitative measure of the probability of finding objects of defined properties within a distance of interest [9, 10]. The concept of autocorrelation is mainly applied in fields such as geography, economics, ecology or meteorology to describe the spatial distribution of features. The idea of a molecular descriptor based on the autocorrelation concept was first introduced into the field of cheminformatics by Moreau and Broto in 1980 [11] with the ATS (autocorrelation of a topological structure) descriptor. For this approach, the atoms of a molecule were represented by properties such as atomic mass or partial charge. The distance between atoms was measured as the number of bonds between the respective atoms (topological distance). 

In the above formula, D denotes that the summation goes over aU hydrogen donor surfaces, qo is the partial charge on H-bonding donor atoms, Sd is the solvent-accessible surface area of H-bonding donor atoms, and Stot is the total solvent-accessible molecular surface area. The electrostatic descriptors have intuitive interpretations. Their inclusion in QSAR models makes easy and clear analysis of the phenomenon under investigation. 

Delgado reports a QSPR model [49] for the prediction of log(l/S) for a set of 50 chlorinated hydrocarbons including chlorinated benzenes, dibenzo-p-dioxins and PCBs. The model involves only two molecular descriptors, one geometry-dependent descriptor and one charged partial surface area (CPSA) descriptor. The geometric descriptor is the area of the shadow of the molecule projected on a plane defined by the X and Y axes XY shadow) the CPSA descriptor is the surface weighted atomic partial negative surface area (WNSA-3). The model has a squared correlation coefficient of 0.97 and standard deviation of 0.45 log units. 

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