Representing Facts — Descriptors

Descriptors are used to store the facts of RTXPS. These descriptors can be either directly entered or can be assigned by the inference engine as a result of data evaluation. The descriptor includes enumerated methods to update its values in the appropriate context. A descriptor basically incorporates values and units, questions, as well as links to rules, functions, or models. A simple example of a descriptor is as follows  

Q What is the average retention time, in days, Q for the reservoir retention time is the theoretical 

Q period the average volume of water spends in the reservoir, 

Q estimated as the ratio of volume to through flow. ENDDESCRIPTOR 

This descriptor has the unique identifier (retention time) and provides values (V) in text format that are associated with a particular range of numerical values in days (U) and a link to a particular rule (R). The (Q) section defines the text for the interaction with the user. 

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Chirality codes are used to represent molecular chirality by a fixed number of de-.scriptors. Thc.se descriptors can then be correlated with molecular properties by way of statistical methods or artificial neural networks, for example. The importance of using descriptors that take different values for opposite enantiomers resides in the fact that observable properties are often different for opposite enantiomers. 

Electric polarization, dipole moments and other related physical quantities, such as multipole moments and polarizabilities, constitute another group of both local and molecular descriptors, which can be defined either in terms of classical physics or quantum mechanics. They encode information about the charge distribution in molecules [Bbttcher et al, 1973]. They are particularly important in modelling solvation properties of compounds which depend on solute/solvent interactions and in fact are frequently used to represent the -> dipolarity/polarizability term in - linear solvation energy relationships. Moreover, they can be used to model the polar interactions which contribute to the determination of the -> lipophilicity of compounds. 

It must be noted that the invariance to rotation of G-WHIM descriptors, i.e. the independence of any molecular alignment rule, is obtained if the grid points are dense enough. In fact, a too sparse distribution of grid points represents an inadequate sampling of the ideal scalar field and is not able to guarantee that the calculated scalar field is representative of the ideal scalar field in such a way as to preserve rotational invariance. 

Steric descriptors and/or -> size descriptors representing the volume of a molecule. The volume of a molecule can be derived from experimental observation such as the volume of the unit cell in crystals or the molar volume of a solution or from theoretical calculations. In fact, analytical and numerical approaches have been proposed for the calculation of molecular volume where the measure depends directly on the definition of - molecular surface-, -> van der Waals volume and -> solvent-excluded volume are two volume descriptors based on van der Waals surface and solvent-accessible surface, respectively. 

Describing a fact, property, or a situation this leads us to the topic of Mathematical Descriptors that are able to represent such information in a computer program. 

In fact, the infrared spectrnm is nothing else than a molecular descriptor that represents a certain property of the molecule its vibrational behavior under infrared radiation. If we look from this standpoint at molecular descriptors, several well-known strnctnral descriptors are already used in the day-to-day laboratory of a scientist. 

The objective of any statistical modelling is not to obtain correlations but to account for mechanistic relationships. A good model describes the observed activity or effects with only few, generally < 3, process-related properties. In contrast, a model with a large number of arbitrary variables is highly likely to represent a chance correlation and will reflect a lack of expertise in the application of statistical tools. It is a trivial fact that increasing the number of descriptor variables ultimately leads to statistically perfect corre-... 

Rule 10 perhaps, in part, overlaps to a degree with rnle 1. Formally speaking, any construction that does not depend on vertex labels or particnlar ways a graph is represented qnalifies as a strnctnral descriptor. A constrnction, in fact, need not even be very complex to resnlt in a descriptor that need not have a simple direct structural interpretation. An illnstration is the Wiener nnmber W, for which Platt tried to find some simple structural interpretation in terms of molecnlar volume [69]. The emphasis here is on clearly interpretable terms of familiar structural concepts otherwise, they will have limited use in onr nnderstanding of the structure-property relationship that they are supposed to explain. On rules 11 and 12, we have previously commented. 

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