Quantitative Structure–Activity Relationships

The concept of quantitative structure-activity relationships was first described in a qualitative way in the nineteenth century and later in a more quantitative way by Hansch and Fujita. It is an attempt to describe structure-activity relationships with mathematical models. For a set of structurally related test compounds, the equi-effective dose inducing a given biological effect is related linearly or quadratically to the logarithm of the octanol/water partition coefficient and the Hammett constant, which describes the electronic properties of substituents on a given scaffold. A mathematical correlation model is calculated by regression analysis. 3D QSAR methods have been developed to consider and correlate the spatial structure of compounds beyond their molecular topology. The aligned molecules are embedded in a regularly spaced grid and their properties are explored using an interacting probe. The probe is systematically placed at all grid points and a molecular interaction field is calculated around the aligned molecules using a distance-dependent property potential. Typically, Lennard-Jones and Coulomb potentials are evaluated and the generated data table for all molecules of the training data set is correlated by a partial least squares technique. The derived CoFMA correlation model can be used to predict the biological properties of novel ligands not included in the training data set. Strict criteria must be in place for the control of the statistical significance of the inferred correlations. Other property fields beyond Lennard-Jones and Coulomb potentials can be applied. These have mathematically different functional forms. With respect to the prediction of binding affinity, hydrophobic properties implicitly reflect an entropic contribution to binding. This is particularly difficult to account for in property fields. QSAR analysis is only a relative comparison of molecules with respect to the biological property under consideration. A dependence on a particular descriptor across a compound series can only be expected if the property related to that descriptor is varied in the series. QSAR methods only interpolate and never extrapolate beyond the range of molecular properties reflected by the training set. A number of alternative 3D QSAR approaches have been developed that use different types of fields or attempt to incorporate protein information into the analysis. Comparative molecular field analyses can be evaluated in a graphical manner. Results are displayed as contours around the molecules, indicating where the change in a particular property is either parallel or opposite to the changes in the biological property in the data set. The graphical information can be directly translated into the design of modified molecules. This helps the medicinal chemist to systematically optimize a given lead structure. https://sn.pub/j7pt6b