Complex descriptors

These "complex" descriptors could be placed In other categories as well ... 

As the values of the leverage matrix are sensitive to the whole molecule structure, they automatically contain information about the molecular complexity, which is a function of the size, symmetry, elemental molecular composition, molecular branching, and centricity. Thus, 3D complexity descriptors can be obtained from this matrix as total and standardized information content ... 

Comparisons among molecular descriptors and among different classes of descriptors are important for at least two reasons (a) to enhance the comprehension of the chemical meaning of complex descriptors by comparing them with other more interpretable descriptors (b) to evaluate their different prediction ability relatively to the different kinds of response to be modeled. 

Often the context implies a particular value of a descriptor, which may then be omitted or descriptors may be dropped if they are not relevant. Second or subsequent movements in complex or composite landslides can be described by repeating terms. Descriptors, which are the same as those for the first movement, may then be omitted from the name. The Frank Shde, for instance, was a complex, extremely rapid, dry rock-fad debris-flow. The type of material may be connected to its type of movement by a hyphen as in debris-flow or left unhyphenated when there is no ambiguity as in the Frank rock fall. The sequence of types of movement, fall then flow, indicates the sequence of movements in the landslide the addition of the complex descriptor to the name distinguishes the landslide from a composite rock-fall debris-flow. The full name of the Frank Slide as given above, implies that the debris flow was both extremely... 

Elementary descriptors are simple first-rank descriptors. They generate composite descriptors in QL. Simple composite descriptors of rank 2-4 consist of 2-4 elementary descriptors. Second-rank descriptors are of four types (SD-LD, SDj-SDj, SD-BD and LD-BD), third-rank descriptors are of three types (SDj-LD-SD2, SD,-LD-BD and SDj-SDj-BD), and fourth-rank descriptors are of one type (SD,-LD-SDj-BD). In all, 11 types of simple descriptors of rank 1-4 are defined in QL. Upon translation, a primary QL representation of a stmcture is corrstracted as a list of fourth-rank descriptors where all of the SDs are numbered. Later ort, these descriptors generate the descriptors of all other ranks therefore, the fortrth-rank descriptors are also referred to as basic descriptors. Descriptors of rank 5 and higher consist of two or more basic descriptors they are referred to as complex descriptors. 

As the rank increases, the dispersion of the compound properties in the descriptors also grows. The simpler the descriptor, the better its extrapolation (prediction) ability more complex descriptors show a better interpolation (recognition) ability. For example, using a CHj group permits a prediction of the activity of many organic compounds. However, for this prediction to be sufficiently accurate, one should take into consideration more complex stractural fragments, too. 

Many classes of natural product possess heterocyclic components (e.g. alkaloids, carbohydrates). However, their structures are often complex, and although structure-based names derived by using the principles outlined in the foregoing sections can be devised, such names tend to be impossibly cumbersome. Furthermore, the properties of complex natural product structures are often closely bound up with their stereochemistry, and for a molecule containing a number of asymmetric elements the specification of a particular stereoisomer by using the fundamental descriptors (R/S, EjZ) is a job few chemists relish. 

Modern ab initio calculations daily confirm the usefulness of the orbital-based quantal perspective as a basis for predicting complex chemical phenomena. In this framework the fundamental descriptors of the orbital filling sequence are the... 

The term fine chemicals is widely used (abused ) as a descriptor for an enormous array of chemicals produced at small scale and is frequently assumed to infer a significant added value of the product derived from the degree of complexity (number of functional groups, geometric isomers, and enantiomers) and precision in their manufacture. Whether the term fine chemicals refers to the finesse of the chemistry or to the small scale of manufacture is far from clear. However, in order to assist our discussion the following division can be adopted [2] ... 

A single-event microkinetic description of complex feedstock conversion allows a fundamental understanding of the occurring phenomena. The limited munber of reaction families results in a tractable number of feedstock independent kinetic parameters. The catalyst dependence of these parameters can be filtered out from these parameters using catalyst descriptors such as the total number of acid sites and the alkene standard protonation enthalpy or by accounting for the shape-selective effects. Relumped single-event microkinetics account for the full reaction network on molecular level and allow to adequately describe typical industrial hydrocracking data. 

The concept of property space is progressively being used to gain a deeper understanding of the dynamic behavior of a single compound in different media (as we illustrate below with acetylcholine, see Section 1.4.2) or bound to biological targets (the carnosine-carnosinase complex, see Section 1.4.3), but it can be used also with a set of compounds to derive fertile descriptors for dynamic QSAR analyses (4D QSAR, see Section 1.4.4). 

In Section 42.2 we have discussed that queuing theory may provide a good qualitative picture of the behaviour of queues in an analytical laboratory. However the analytical process is too complex to obtain good quantitative predictions. As this was also true for queuing problems in other fields, another branch of Operations Research, called Discrete Event Simulation emerged. The basic principle of discrete event simulation is to generate sample arrivals. Each sample is characterized by a number of descriptors, e.g. one of those descriptors is the analysis time. In the jargon of simulation software, a sample is an object, with a number of attributes (e.g. analysis time) and associated values (e.g. 30 min). Other objects are e.g. instruments and analysts. A possible attribute is a list of the analytical... 

As an extension of perceptron-like networks MLF networks can be used for non-linear classification tasks. They can however also be used to model complex non-linear relationships between two related series of data, descriptor or independent variables (X matrix) and their associated predictor or dependent variables (Y matrix). Used as such they are an alternative for other numerical non-linear methods. Each row of the X-data table corresponds to an input or descriptor pattern. The corresponding row in the Y matrix is the associated desired output or solution pattern. A detailed description can be found in Refs. [9,10,12-18]. 

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