Structure-based descriptors

The equilibrium population is said to have a structure when a substantial fraction of the molecules adopts similar conformations. But the phrase lacking structure does not imply that individual molecules comprising the ensemble lack a conformation rather, the population is too heterogeneous to be readily characterized using a coherent, structure-based descriptor. The unfolded state resists ready characterization because it is so diverse. Typical biophysical methods report ensemble-... 

This chapter deals with the concept of biological fingerprints that have been described as a better way to describe compounds of biological interest. It includes examples of how these descriptors are far more powerful than structure-based descriptors in both differentiating compounds and enabling the selection of the best lead compounds, and can provide a way to investigate in vitro-in vivo relationships such as for ADRs (adverse drug reactions). 

Patankar, S.J. and Jurs, P.C. (2003b) Classification of inhibitors of protein tyrosine phosphatase IB using molecular structure based descriptors. 

Resistance Reversal Agents Using Structure-Based Descriptors and Linear Discriminant Analysis. 

A particularly good selection of physical properties may be spectra, because they are known to depend strongly on the chemical structure. In fact, different types of spectra carry different kinds of structural information, NMR spectra characterize individual carbon atoms in their molecular environment. They therefore correspond quite closely to fragment-based descriptors, as underlined by the success of approaches to predict NMR spectra by fragment codes (see Section 10.2.3). 

The abbreviation QSAR stands for quantitative structure-activity relationships. QSPR means quantitative structure-property relationships. As the properties of an organic compound usually cannot be predicted directly from its molecular structure, an indirect approach Is used to overcome this problem. In the first step numerical descriptors encoding information about the molecular structure are calculated for a set of compounds. Secondly, statistical methods and artificial neural network models are used to predict the property or activity of interest, based on these descriptors or a suitable subset. A typical QSAR/QSPR study comprises the following steps structure entry or start from an existing structure database), descriptor calculation, descriptor selection, model building, model validation. 

Brown R D and Y C Martin 1996. Use of Structure-Activity Data to Compare Structure-Base Clustering Methods and Descriptors for Use in Compound Selection. Journal of Chemia Information and Computer Science 36 572-583. 

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. 

R.D. Brown and Y.C. Martin, Use of structure-activity data to compare structure-based clustering methods and descriptors for use in compound selection. J. Chem. Inf. Comput. Sci., 36 (1996) 572-584. 

In many cases of practical interest, no theoretically based mathematical equations exist for the relationships between x and y we sometimes know but often only assume that relationships exist. Examples are for instance modeling of the boiling point or the toxicity of chemical compounds by variables derived from the chemical structure (molecular descriptors). Investigation of quantitative structure-property or structure-activity relationships (QSPR/QSAR) by this approach requires multivariate calibration methods. For such purely empirical models—often with many variables—the... 

Fig. 4.1 Virtual screening tools can be categorized by the compound data to be screened (compound collection, combinatorial library, chemistry space) and the query type (structure-based, ligand-based, descriptor-based, pharmacophore-based). The output is always a list of compounds together with a score quantifying the fit to the query.

Mason, J.S. and Beno, B.R. Library design using BCUT chemistry-space descriptors and multiple four-point pharmacophore fingerprints simultaneous optimization and structure-based diversity. /. Mol. 

In addition to methods that rely on experimental inputs, there are prediction tools that are based on structure alone. A number of different techniques have been used to correlate a variety of structure-derived descriptors with observed solubilities, including linear regression [20-25] and neural networks [26-36]. Interestingly, despite... 

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