On Characterization of Chemical Structure

Abstract

We briefly review characterization of chemical structure as evolving from the early work on the connectivity index to the latest work on a characterization of 3-D structures, including characterization of the folding of model proteins.

In silico Molecular Docking and Design of Anti-Hepatitis B Drugs

“…To get an insight into the structure-activity relationship molecular descriptors are needed that can effectively characterize molecular size, molecular branching or the variations in molecular shapes, and can influence the structure and its activities [37]. It is very essential in drug discovery and design.…”

QSAR study on murine recombinant isozyme mCAXIII: topological vs structural descriptors

“…Therefore, we have to make use of the recommendations made by Randic 39,40 for resolving the problem of co linearity. often strongly correlates and in many structure-property-activity studies 2 χ has been discarded.…”

“…The criteria for inclusion or exclusion of descriptors should not be based on parallelism between descriptors even if overwhelming, but should be based on whether the part in which two descriptors disagree is or is not relevant for the characterization of the property considered .If the part in which the second descriptor differ from the first, regardless of how small it is, is relevant for the property under consideration, then the descriptor should be included. Randic 39,40 further stated that the selection of descriptors to be used in structure-property-activity studies should not be delegated solely to computers, although statistical criteria will continue to be useful for preliminary screening of descriptors taken from a large pool. Often in an automated selection of descriptors, a descriptor will be discarded because it is highly correlated with another descriptor already selected.…”

Vector Dot Product Used for Reduced Three Independent Variables of Multivariate Regression to a Linear Regression With One Independent Variable: Alcohols Used Like a Model

“…In the QSPR multivariate regression equations, the real significance of all factors and signs affecting each independent variable are obtained if orthogonal procedure 1 is carry on, or the reductions number of poor significant independent variables by means of Principal Component Analysis (PSA) 2 is applied. By other hand, is very important to considered the number of independent variables used in the mathematical regressions, its must be in accordance with the number of cases treatise, if not, the correlation determination coefficients (R 2 ) value is false by excess 3 .…”