Atomic properties partial charges

Two other atomic properties have been used in the definition of atom type, thereby increasing its fuzziness relative to that in the ap and tt descriptors - atomic log P contribution (yielding hydrophobic pairs, hps, and torsions, hts) and partial atomic charges (charge pairs, cps, and charge torsions, cts). 

Many phenomena ask for local, site-specific properties of a molecule such as the partial charge on a specific atom in a molecule or the hydrogen bond donor ability of a certain OH group. It would be highly desirable to have methods as simple as an additivity model to estimate such site-specific molecular properties. 

The chirality code of a molecule is based on atomic properties and on the 3D structure. Examples of atomic properties arc partial atomic charges and polarizabilities, which are easily accessible by fast empirical methods contained in the PETRA package. Other atomic properties, calculated by other methods, can in principle be used. It is convenient, however, if the chosen atomic property discriminates as much as possible between non-equivalent atoms. 3D molecular structures are easily generated by the GORINA software package (see Section 2.13), but other sources of 3D structures can be used as well. 

Figure 8-11. iD structure and representation offcoccia) versus u for (fi)-4 and [S)-4 at two different conformations (a and b) sampled at 50 evenly separated values between -0,100 e A and i-0.100 e A. Partial atomic charge was used as the atomic property. 

In the present work, we shall investigate the problem of the amount of correlation accounted for in the DF formalism by comparing the molecular electrostatic potentials (MEPs) and dipole moments of CO and N2O calculated by DF and ab initio methods. It is indeed well known that the calculated dipole moment rf these compounds is critically dependent on the level of theory implemented and, in particular, that introduction of correlation is essential for an accurate prediction [13,14]. As the MEP property reflects reliably the partial charges distribution on the atoms of the molecule, it is expected that the MEP will exhibit a similar dependence and that its gross features correlate with the changes in the value of dipole moment when switching from one level of theory to the other. Such a behavior has indeed been reported recently by Luque et al. [15], but their study is limited to the ab initio method and we found it worthwhile to extend it to the DF formalism. Finally, the proton affinity and the site of protonation of N2O, as calculated by both DF and ab initio methods, will be reported. 

Analysis of the Hansch descriptors illustrates that increasing the lipophilicity property for this series of compounds will increase the bioactivity in a more dramatic fashion than increasing the Tafts steric parameter. Increasing the water accessible surface area (MOE descriptor) will increase the bioactivity of the compounds. But there is more than just adding lipophilicity and water accessible surface area to the molecules to increase binding affinity. A comparison of the substituents to the bioactivities illustrates that the bulkier X and Y groups increase bioactivity in conjunction with atomic partial charge distribution of the substituents. 

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. 

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. 

To do this, we define two additional multi dimensional spaces B and C (Figure 1). Space B contains the values of the catalyst descriptors that pertain to these catalysts e.g. backbone flexibility, partial charge on the metal atom, lipophilicity) as well as the reaction conditions (temperature, pressure, solvent type, and so on). Finally, space C contains the catalyst figures of merit (i.e., the TON, TOF, product selectivity, price, and so forth). Spaces B and C are continuous, and are arranged such that each dimension in each space represents one property. 

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