Application of 4D-QSAR Analysis to a Set of Prostaglandin, PGF2α, Analogs
Four-dimensional Quantitative Structure-Activity Relationship (4D-QSAR) analysis is a method developed recently to determine molecular similarity, diversity, and construct three-dimensional structure-activity relationships (3D-QSARs)1. 4D-QSAR analysis incorporates conformational and alignment freedom into the development of 3D-QSAR models for training sets of structure-activity data by performing ensemble averaging, the fourth “dimension”. The difference between 4D-QSAR and 3D-QSAR is that instead of examining a single conformation and alignment, an ensemble of conformations and alignments over a short period of time is examined. The descriptors in 4D-QSAR analysis are derived from measures of grid cell (spatial) occupancy of the atoms present in each molecule in the training set, realized from sampling of conformation and alignment spaces. Grid cell occupancy descriptor can be generated for any atom type, group, and/or model phamracophore. Serial use of partial-least squares, (PLS), regression and a Genetic Algorithm, (GA), is used to perform data reduction and identify the manifold of top 3D-QSAR models for the training set. The unique manifold of 3D-QSAR models is determined by computing the extent of orthogonality in the residuals of error among the most significant 3D-QSAR models generated by the GA. Additionally, a single “active” conformation can be postulated for each compound in the training set, which can be combined with optimal alignment for use in other molecular design applications, including other 3D-QSAR methods. The influence of the conformational entropy on the activity of each compound can also be estimated.
Receptor independent (RI) 4D-QSAR was successfully applied to a set of 42 Prostaglandin, PGF2α, analogs, with antinidatory activity.
Two (RI) 4D-QSAR studies were carried out. The first study has been described in reference (1) in great detail, and only the second study has been described here.