Chemical information content

Jeffrey W. Godden and Jurgen Bajorath, Analysis of Chemical Information Content Using Shannon Entropy. 

Despite these caveats, IR is an excellent tool for API process monitoring because of its chemical information content. This is particularly valuable in early-stage development when it can yield crucial information about unexpected reaction intermediates and side reactions and therefore lead directly to a more robust process. Commercial instrumentation is widely available for this purpose [78] and development of cheaper, smaller and more rugged instrumentation continues apace [79]. For example, a miniaturised mid-infrared spectrometer and... 

Tripos, Inc. Originally the major provider cf molecular modeling software. Tripos now offers chemical information content in the form of databases and the tools to manage them. These include the following ... 

The above considerations also hold for different hierarchical descriptions of a system at a given level, i.e. the same level is traversed by an inner hierarchical organization because of different descriptions of the same elements. The molecular representations are hierarchical descriptions of the molecular system, therefore, derived from the different representations of the molecular structure several - molecular descriptors are calculated with different chemical information contents. 

Although similarity search tools can be applied on the basis of individual reference molecules, their performance generally increases when multiple reference compounds are available, which then makes it possible to apply data fusion techniques to merge individual results [19]. Generally, the chemical information content of search calculations is increasing with the availability of multiple active reference compounds. 

Natural products continue to be an important resource for the discovery of therapeutically relevant molecules. In addition, naturally occurring molecules present a rich source of chemical diversity and information. This makes them an interesting target for chemoinformatic investigations, despite the fact that they are sometimes too large or chanicaUy too complex for the analysis of structure-activity relationships, molecular similarity calculations, or other computational studies. Recently, efforts were made to statistically analyze collections of natural products, explore their chemical information content, and compare them to synthetic compounds. 

Subsequently, a high-sensitivity back-illuminated, deep-depletion CCD-based Raman spectrometer (with 785-nm excitation and a CCD quantum efficiency of 85-90% at this wavelength) was used (instead of the UV spectrometer) for this application in order to improve microconstant determination and, more importantly, to allow qualitative identification of individual microspecies [94]. RS is particularly promising for this work, as it provides high chemical information content in the form of vibrational fingerprints as well as tolerates the presence of water. Just a few years ago, RS could not be considered for this application due to the historically high detection levels and problems with fluorescence. 

Table 5.36 shows the main features of NSOM. Optical fibre tips for NSOM with high light transmission permit surface analytical and spectroscopic applications (fiuorescence imaging, Raman) with high spatial resolution (ca. 30 nm) and high chemical information content [337], NSOM overcomes critical measurement limitations of both far-fleld vibrational microscopes (low spatial resolution) and scanned probe microscopes (lack of chemical specificity). NSOM offers conventional optical characterisation and contrast mechanisms with the resolution of SPM. The spatial resolution of this relatively new technique is almost competitive with that of SEM. Consequently, NSOM is expected to become a serious alternative for SEM, since it is non-destructive if visible light is used, and allows visualisation of specimens in air. The technique holds considerable promise for the future. At the present time it is still quite expensive. 

We will describe below the SE formalism in detail and explain how it can be used to estimate chemical information content based on histogram representations of feature value distributions. Examples from our work and studies by others will be used to illustrate key aspects of chemical information content analysis. Although we focus on the Shannon entropy concept, other measures of information content will also be discussed, albeit briefly. We will also explain why it has been useful to extend the Shannon entropy concept by introducing differential Shannon entropy (DSE) to facilitate large-scale analysis and comparison of chemical features. The DSE formalism has ultimately led to the introduction of the SE-DSE metric. 

---