Four-dimensional quantitative structure-activity relationship analysis of a series of interphenylene 7-oxabicycloheptane oxazole thromboxane A2 receptor antagonists.
A series of 39 (a training set of 29 and a test set of 10) interphenylene 7-oxabicyclo [2.2.1]heptane oxazole thromboxane A2 (TXA2) receptor antagonists were studied using four-dimensional quantitative structure-activity relationship (4D-QSAR) analysis. Two thousand conformations of each analogue were sampled to generate a conformational energy profile (CEP) from a molecular dynamic simulation (MDS) of 100,000 trajectory states. Each conformation was placed in a grid cell lattice for each of six trial alignments. Cubic grid cell sizes of 1 and 2 A were considered. The frequency of occupation of each grid cell was computed for each of seven types of pharmacophoric group classes of atoms of each compound. These grid cell occupancy descriptors (GCODs) were then used as independent variables in constructing three-dimensional (3D)-QSAR models after data reduction. The types of data reduction included doing no reducing, reduction based on individual GCOD correlation with activity, and reduction from minimum variance constraints over the GCOD population. The 3D-QSAR models were generated and evaluated by a scheme that combines a genetic algorithm (GA) optimization with partial least squares (PLS) regression. The 3D-QSAR models were evaluated by cross-validation using the leave-one-out technique. The cross-validated correlation coefficient, Q2, ranged from 0.27 to 0.86. The models are not from chance correlation because a scrambled data set was generated and evaluated (Q2 = 0.25-0.37). A composite 3D-QSAR model was constructed using the best models derived from GCODs of both 1 and 2 A grid cell size lattices. The 3D-QSAR models provide detailed 3D pharmacophore requirements in terms of atom types and corresponding locations needed for high TXA2 inhibition activity. Specific sites in space that should not be occupied by an active inhibitor are also specified. The GCOD measures for the compounds in the training set permit reference points regarding which pharmacophore sites can provide the largest boosts in inhibition activity relative to the existing analogues.
Albuquerque
M G
MG
Laboratory of Molecular Modeling and Design, College of Pharmacy, University of Illinois at Chicago 60612-7231, USA.
Hopfinger
A J
AJ
Barreiro
E J
EJ
de Alencastro
R B
RB
D015195:Drug Design; D008956:Models, Chemical; D010080:Oxazoles / Q000138:chemical synthesis* / Q000494:pharmacology; D011487:Protein Conformation; D017482:Receptors, Thromboxane / Q000037:antagonists & inhibitors*; D013329:Structure-Activity Relationship
D010080:Oxazoles; D017482:Receptors, Thromboxane