Methods for Applying the Quantitative Structure-Activity Relationship Paradigm

Abstract

There are several Quantitative Structure-Activity Relationship (QSAR) methods to assist in the design of compounds for medicinal use. Owing to the different QSAR methodologies, deciding which QSAR method to use depends on the composition of system of interest and the desired results. The relationship between a compound's binding affinity/activity to its structural properties was first noted in the 1930s by Hammett and later refined by Hansch and Fujita in the mid-1960s. In 1988 Cramer and coworkers created Comparative Molecular Field Analysis (CoMFA) incorporating the three-dimensional (3D) aspects of the compounds, specifically the electrostatic fields of the compound, into the QSAR model. Hopfinger and coworkers included an additional dimension to 3D-QSAR methodology in 1997 that eliminated the question of "Which conformation to use in a QSAR study?", creating 4D-QSAR. In 1999 Chemical Computing Group Inc. (CCG) developed the Binary-QSAR methodology and added novel 3D-QSAR descriptors to the traditional QSAR model allowing the 3D properties of compounds to be incorporated into the traditional QSAR model. Recently CCG released Probabilistic Receptor Potentials to calculate the substrate's atomic preferences in the active site. These potentials are constructed by fitting analytical functions to experimental properties of the substrates using knowledge-based methods. An overview of these and other QSAR methods will be discussed along with an in-depth examination of the methodologies used to construct QSAR models. Also, included in this chapter is a case study of molecules used to create QSAR models utilizing different methodologies and QSAR programs.

Monitoring and Modeling of Chlorophyll-a Dynamics in a Eutrophic Lake: M'koa Lake (Jacqueville, Ivory Coast)

“…The coefficient of determination R² gives an evaluation of the dispersion of the calculated values around the experimental ones. The quality of the modeling is better when the points evaluated by the coefficient R² are close to the line of adjustment [26]. R² is expressed as follows:…”

Quantitative Activity Structure Relationship (QSAR) of a Series of Azetidinones Derived from Dapsone by the Method of Density Functional Theory (DFT)

“…The determination's coefficient R 2 gives an evaluation of the dispersion of theoretical values around the experimental ones. The quality of the modeling is better when the points are close to the fitting line [22]. The adjustment of the points to this line can be evaluated by the coefficient of determination.…”

Quantitative Structure antifungal Activity Relationship (QSAR) study of a series of Schiff bases derivatives from4- aminobenzenesulphonamide by DFT method

“…The squared correlation coefficient gave an evaluation of the dispersion of the theoretical values around the experimental values. The quality of modeling was better when the points were close to the fitted line [28].The adjustment of points in this straight line can be evaluated by the correlation coefficient. The correlation coefficient R 2 was given by the following equation (2):…”