Molecular structure bases

Diamantane-based polymers are synthesized to take advantage of their stiffness, chemical and thermal stability, high glass transition temperature, improved solubility in organic solvents, and retention of their physical properties at high temperatures. All these special properties result from their diamantane-based molecular structure [90]. Polyamides are high-temperature polymers with a broad range of applications in different scientific and industrial fields. However, their process is very difficult because of poor solubility and lack of adequate thermal stability retention [90]. Incorporation of 1,6- or... 

The N-terminal peptide fragment of des-angiotensinogen Val-Ile-His-Asn contains two strongly hydrophobic amino acid residues on the N-terminal site of His-3. The potentiometric data have shown that the NiH.2L complex with this albumin-like sequence is more than two orders of magnitude more stable than the respective complex with Gly-Gly-His.1744 The NMR-based molecular structure has shown that the side chains of Val-1 and lie-2 form a well-ordered hydrophobic fence (Figure 21) shielding one side of the coordination plane from the bulk of... 

I. Gutman, Preface to a special issue entitled Graph-based molecular structure-descriptors— theory and applications, Ind. J. Chem. 42A (2003) 1197-1198. 

The ability to explore carbohydrate molecular diversity has taken great strides in recent years. Viable solid phase and solution phase methods now exist for rapidly accessing some of the broad universe of carbohydrate-based molecular structures. It is now the job of chemists to exploit these methods for the discovery of novel medicinal, agricultural and industrial agents. 

The various arrangements of carbon atoms can be categorised into senes , which describe a common molecular structure. The series are based on four main categories which refer to... 

As this short example shows. PDB files use different syntax for different records and both writing and reading such files require much effort. Another problem is the extensibility of this format to handle new kinds of information, which further complicates the file structure. The Protein Data Bank has been faced with the consequences - the existing legacy data comply with several different PDB formats, so they are not uniform and they arc more difEcuh to handle (145, 155, 157]. As mentioned in Section 2,9.7.1, there is a much more flexible and general way of representing molecular structure codes and associated information - the STAR file format and the file formats based on it. 

Many problems in force field investigations arise from the calculation of Coulomb interactions with fixed charges, thereby neglecting possible mutual polarization. With that obvious drawback in mind, Ulrich Sternberg developed the COSMOS (Computer Simulation of Molecular Structures) force field [30], which extends a classical molecular mechanics force field by serai-empirical charge calculation based on bond polarization theory [31, 32]. This approach has the advantage that the atomic charges depend on the three-dimensional structure of the molecule. Parts of the functional form of COSMOS were taken from the PIMM force field of Lindner et al., which combines self-consistent field theory for r-orbitals ( nr-SCF) with molecular mechanics [33, 34]. 

A challenging task in material science as well as in pharmaceutical research is to custom tailor a compound s properties. George S. Hammond stated that the most fundamental and lasting objective of synthesis is not production of new compounds, but production of properties (Norris Award Lecture, 1968). The molecular structure of an organic or inorganic compound determines its properties. Nevertheless, methods for the direct prediction of a compound s properties based on its molecular structure are usually not available (Figure 8-1). Therefore, the establishment of Quantitative Structure-Property Relationships (QSPRs) and Quantitative Structure-Activity Relationships (QSARs) uses an indirect approach in order to tackle this problem. In the first step, numerical descriptors encoding information about the molecular structure are calculated for a set of compounds. Secondly, statistical and artificial neural network models are used to predict the property or activity of interest based on these descriptors or a suitable subset. 

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. 

A series of monographs and correlation tables exist for the interpretation of vibrational spectra [52-55]. However, the relationship of frequency characteristics and structural features is rather complicated and the number of known correlations between IR spectra and structures is very large. In many cases, it is almost impossible to analyze a molecular structure without the aid of computational techniques. Existing approaches are mainly based on the interpretation of vibrational spectra by mathematical models, rule sets, and decision trees or fuzzy logic approaches. 

A proper representation of the molecular structure is crucial for the prediction of spectra. Fragment-based methods, topological descriptors, physicochemical descriptors, and 3D descriptors have been used for this endeavor. 

Judson R S, E P Jaeger and A M Treasurywala 1994. A Genetic Algorithm-Based Method for Dockin Flexible Molecules. Journal of Molecular Structure Theochem 114 191-206. 

Kuntz I D, E C Meng and B K Shoichet 1994. Structure-Based Molecular Design. Accounts of Chemici Research 27 117-123. 

Structure-property relationships are qualitative or quantitative empirically defined relationships between molecular structure and observed properties. In some cases, this may seem to duplicate statistical mechanical or quantum mechanical results. However, structure-property relationships need not be based on any rigorous theoretical principles. 

In Chapter 1 we saw that a major achievement of the first half of the twentieth cen tury was the picture of atomic and molecular structure revealed by quantum mechan ICS In this the last chapter we examine the major achievement of the second half of that century—a molecular view of genetics based on the structure and biochemistry of nucleic acids... 

Molecular ion. An ion formed by the removal (positive ions) or addition (negative ions) of one or more electrons from a molecule without fragmentation of the molecular structure. The mass of this ion corresponds to the sum of the masses of the most abundant naturally occurring isotopes of the various atoms that make up the molecule (with a correction for the masses of the electrons lost or gained). For example, the mass of the molecular ion of the ethyl bromide CzHjBr will be 2 x 12 plus 5 x 1.0078246 plus 78.91839 minus the mass of the electron (m ). This is equal to 107.95751p -m, the unit of atomic mass based on the standard that the mass of the isotope = 12.000000 exactly. 

In the last section we examined some of the categories into which polymers can be classified. Various aspects of molecular structure were used as the basis for classification in that section. Next we shall consider the chemical reactions that produce the molecules as a basis for classification. The objective of this discussion is simply to provide some orientation and to introduce some typical polymers. For this purpose a number of polymers may be classified as either addition or condensation polymers. Each of these classes of polymers are discussed in detail in Part II of this book, specifically Chaps. 5 and 6 for condensation and addition, respectively. Even though these categories are based on the reactions which produce the polymers, it should not be inferred that only two types of polymerization reactions exist. We have to start somewhere, and these two important categories are the usual place to begin. 

As discussed in Sec. 4, the icomplex function of temperature, pressure, and equilibrium vapor- and hquid-phase compositions. However, for mixtures of compounds of similar molecular structure and size, the K value depends mainly on temperature and pressure. For example, several major graphical ilight-hydrocarbon systems. The easiest to use are the DePriester charts [Chem. Eng. Prog. Symp. Ser 7, 49, 1 (1953)], which cover 12 hydrocarbons (methane, ethylene, ethane, propylene, propane, isobutane, isobutylene, /i-butane, isopentane, /1-pentane, /i-hexane, and /i-heptane). These charts are a simplification of the Kellogg charts [Liquid-Vapor Equilibiia in Mixtures of Light Hydrocarbons, MWK Equilibnum Con.stants, Polyco Data, (1950)] and include additional experimental data. The Kellogg charts, and hence the DePriester charts, are based primarily on the Benedict-Webb-Rubin equation of state [Chem. Eng. Prog., 47,419 (1951) 47, 449 (1951)], which can represent both the liquid and the vapor phases and can predict K values quite accurately when the equation constants are available for the components in question. 

The initial coordinates r(0) are usually obtained from experimentally determined molecular structures, mainly from X-ray crystallography and NMR experiments. Alternatively, the initial coordinates can be based on computer models generated by a variety of modeling techniques (see Chapters 14 and 15). Note, however, that even the experimentally determined strucmres must often undergo some preparation steps before they can be used as initial structures in a dynamic simulation. 

Most of the 2D QSAR methods are based on graph theoretic indices, which have been extensively studied by Randic [29] and Kier and Hall [30,31]. Although these structural indices represent different aspects of molecular structures, their physicochemical meaning is unclear. Successful applications of these topological indices combined with multiple linear regression (MLR) analysis are summarized in Ref. 31. On the other hand, parameters derived from various experiments through chemometric methods have also been used in the study of peptide QSAR, where partial least square (PLS) [32] analysis has been employed [33]. 

There are at the present time many thousands of grades of commercial plastics materials offered for sale throughout the world. Only rarely are the properties of any two of these grades identical, for although the number of chemically distinct species (e.g. polyethylenes, polystyrenes) is limited, there are many variations within each group. Such variations can arise through differences in molecular structure, differences in physical form, the presence of impurities and also in the nature and amount of additives which may have been incorporated into the base polymer. One of the aims of this book is to show how the many different materials arise, to discuss their properties and to show how these properties can to a large extent be explained by consideration of the composition of a plastics material and in particular the molecular structure of the base polymer employed. 

The properties of mesophase pitch-based carbon fibers can vary significantly with fiber texture. Inspection of the cross-section of a circular mesophase fiber usually shows that the graphitic structure converges toward the center of the fiber. This radial texture develops when flow is fully developed during extrusion through the spinnerette. Endo [48] has shown that this texture of mesophase pitch-based carbon fibers is a direct reflection of their underlying molecular structure. 

It has been shown that description of bonding based on the bent-bond concept can be just as successful in describing molecular structure as the hybridization concept. We will, however, use the hybridization terminology. 

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