Structure prediction techniques identification

ML techniques are well situated for the analysis of molecular sequence data. These methods have been applied successfully to a variety problem, ranging from gene identification to protein structure prediction and sequence classihcation (24,25). These techniques have become an important topic for... 

NMR has proven to be one of the most valuable spectroscopic techniques for the identification and characterization of a wide range of organic and inorganic species. It can provide unparalleled information about the chemical composition and structure of molecules. The newer two-dimensional pulse techniques [20] are expanding the capabilities of NMR even further so that in some cases structural prediction can approach that of x-ray crystallography. The use of NMR has traditionally been associated with chemical structure determination or characterization of new compounds produced by synthetic chemists or as an aid in the identification of unknown compounds in the solving of analytical problems. In most instances the compound is dissolved in an appropriate solvent and the spectrum is obtained in the solution state. For materials analysis or surface characterization, it is often impossible or impractical to dissolve the sample. Therefore, the ability to obtain NMR spectra in the solid state is crucial to the extension of this most important spectral technique to the study of surfaces. 

In conclusion, while techniques for the prediction of low-resolution structures have improved, they still have a way to go before structure prediction becomes routine. Nevertheless, this is a very laudable goal because low-resolution structures are of considerable utility both in the identification of biochemical function and in ligand docking. Such efforts will have to be applied on a genomic scale if structure-based approaches to function prediction are to play a role in the post genomic era. A number of such efforts are underway, and doubtless there will be more in the future. 

Although a number of secondary minerals have been predicted to form in weathered CCB materials, few have been positively identified by physical characterization methods. Secondary phases in CCB materials may be difficult or impossible to characterize due to their low abundance and small particle size. Conventional mineral identification methods such as X-ray diffraction (XRD) analysis fail to identify secondary phases that are less than 1-5% by weight of the CCB or are X-ray amorphous. Scanning electron microscopy (SEM) and transmission electron microscopy (TEM), coupled with energy dispersive spectroscopy (EDS), can often identify phases not seen by XRD. Additional analytical methods used to characterize trace secondary phases include infrared (IR) spectroscopy, electron microprobe (EMP) analysis, differential thermal analysis (DTA), and various synchrotron radiation techniques (e.g., micro-XRD, X-ray absorption near-eidge spectroscopy [XANES], X-ray absorption fine-structure [XAFSJ). 

Structural analysis from electronic spectra yields little information because of their relative simplicity. In the 1940s, however, before the advent of more powerful identification techniques, UV/VIS visible spectroscopy was used for structural identification. The study of a great number of spectra of various molecules has revealed correlations between structures and the positions of absorption maxima. The most widely known empirical rules, due to Woodward, Fieser and Scott, involve unsaturated carbonyls, dienes and steroids. Using incremental tables based on various factors and structural features, it is possible to predict the position of the n —> n absorption bands in these conjugated systems (Table 11.3). Agreement between the calculated values and the experimentally determined position of absorption bands is usually good, as can been seen by the following four examples ... 

Advances in NMR spectroscopy have made this technique very effective for identification and structural characterization of reactive intermediates in electrophilic reactions. Nowadays it is a powerful tool which can be used for prediction and evaluation of reactivity of polyfluorinated materials. 

It is common opinion [5] that the use of in silico methods to predict a hypothetical metabolite structure, combined with the most recent experimental techniques, can speed up the process of metabolite identification by focusing experimental work on specific target structures, thus improving the method of metabolite structure confirmation and elucidation. 

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