Descriptors experimental

An extensive series of studies for the prediction of aqueous solubility has been reported in the literature, as summarized by Lipinski et al. [15] and jorgensen and Duffy [16]. These methods can be categorized into three types 1 correlation of solubility with experimentally determined physicochemical properties such as melting point and molecular volume 2) estimation of solubility by group contribution methods and 3) correlation of solubility with descriptors derived from the molecular structure by computational methods. The third approach has been proven to be particularly successful for the prediction of solubility because it does not need experimental descriptors and can therefore be applied to collections of virtual compounds also. 

The model has been claimed to predict the log BB values at a rate of 700 molecules per min, some two orders of magnitude faster than the calculations of Keserii and Molnar. To test the likely accuracy of such predictions, the authors took 105 compounds for which experimental descriptors were used in constructing equation (48) and calculated their log BB values using same with descriptors calculated by the method of Platts and workers. The observed and calculated values agreed with ESD = 0.294, thus indicating that Platts calculation method can be used in conjunction with equation (48) to predict further log BB values faster with an estimated accuracy of around 0.30 to 0.35 log units. 

Molecular descriptors vary gready in both their origins and their applications. They come from both experimental measurements and theoretical computations. Typical molecular descriptors from experimental measurements include logP, aqueous solubility, molar refractivity, dipole moment, polarizability, Hammett substituent constants, and other empirical physicochemical properties. Notice that the majority of experimental descriptors are for entire molecules and come directly from experimental measurements. A few of them, such as various substituent constants, are for molecular fragments attached to certain molecular templates and they are derived from experimental results. 

This chapter provides a tutorial focused on the uses of quantum mechanical descriptors in linear free energy relationships (LFERs). Often, LFERs derived with empirically based (i.e., experimental) descriptors are superior in quality to those derived with quantum mechanical descriptors. However, theoretically based LFERs have some advantages including ease of calculation. The QM... 

Correlation analysis can employ empirical (experimental) descriptors or theoretical descriptors or both. As introduced earlier, theoretical (computational) descriptors offer several advantages over empirical ones. They are... 

In contrast to that, molecular descriptors in the context of computational chemistry are valnes, vectors, or matrices that are calculated from one or more measured or calculated properties of a molecule. For ease of understanding, let us define those descriptors recorded as a result of an analytical technique as experimental descriptors. In contrast to that, we will talk about artificial descriptors when we refer to those that are calculated. 

Experimental descriptors emerge from a fixed experimental design, and their appearance is subject to the physical or chemical limitations of the measurement technique. The advantage of artificial descriptors is that they can be adjusted and fine-tuned easily to fit to a task due to their pure mathematical nature. The only limitation to this approach is the scientist s imagination. However, there are several constraints to be taken into account when selecting or constructing a molecular descriptor. Todeschini and Consonni pointed these out in their book Handbook of Molecular Descriptors [15]. Let us have a closer look at these constraints. 

Correlation with Other Molecular Descriptors — This applies mainly to artificial descriptors. A correlation with experimental descriptors is helpful to produce missing experimental data. 

Before we have a quick look at three of the most important transform methods, we should keep the following in mind. The mathematical theory of transformations is usually related to continuous phenomena for instance, Fourier transform is more exactly described as continuous Fourier transform (CFT). Experimental descriptors, such as signals resulting from instrumental analysis, as well as calculated artificial descriptors require an analysis on basis of discrete intervals. Transformations applied to such descriptors are usually indicated by the term discrete, such as the discrete Fourier transform (DFT). Similarly, efficient algorithms for computing those discrete transforms are typically indicated by the term fast, such as fast Fourier transform (FFT). We will focus in the following on the practical application — that is, on discrete transforms and fast transform algorithms. 

Descriptor/Descriptor Correlation is the property of an artificial molecular descriptor to correlate with at least one experimental descriptor. 

Experimental Descriptor is a descriptor that results from an analytical technique, such as spectrometry. Experimental descriptors emerge from a fixed experimental design, and their appearance is subject to the physical or chemical limitations of the measurement technique. 

These retention patterns have encouraged different research lines trying to find relationships between retention values and compound characteristics. Structural properties can be expressed numerically by using "descriptors" (parameters that try to describe as accurately as possible a molecular structure). Physical properties of the analyte (boiling point, vapour pressure) can also be considered as experimental descriptors. 

Classical QSAR methods focus on correlating experimental activities with experimental descriptors. This allows an identification of important structural features, such as having a pA a value close to 5 or having an electron-withdrawing group in a para-position of a phenyl ring, thereby limiting the field of possible new compounds to... 

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