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 descriptors in 4D-QSAR analysis are the grid cell (spatial) occupancy measures of the atoms composing each molecule in the training set realized from the sampling of conformation and alignment spaces. Grid cell occupancy descriptors can be generated for any atom type, group, and/or model pharmacophore. A single “active” conformation can be postulated for each compound in the training set and combined with the optimal alignment for use in other molecular design applications including other 3D-QSAR methods. The influence of the conformational entropy of each compound on its activity can be estimated. 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 a training set. The unique manifold of 3D-QSAR models is arrived at by computing the extent of orthogonality in the residuals of error among the most significant 3D-QSAR models in the general GA population. Receptor independent (RI) 4D-QSAR analysis has been successfully applied to three training sets:  (a) benzylpyrimidine inhibitors of dihydrofolate reductase, (b) prostaglandin PGF2α antinidatory analogs, and, (c) dipyridodiazepinone inhibitors of HIV-1 reverse transcriptase (RT). Two general findings from these applications are that grid cell occupancy descriptors associated with the “constant” chemical structure of an analog series can be significant in the 3D-QSAR models and that there is an enormous data reduction in constructing 3D-QSAR models. The resultant 3D-QSAR models can be graphically represented by plotting the significant 3D-QSAR grid cells in space along with their descriptor attributes.

The torsion angle motions, generated from molecular dynamics (MD) simulations, of the two aliphatic chains of 1,2‐dimyristoyl‐sn‐glycero‐3‐phosphatidylcholine (DMPC) in its lipid monolayer were evaluated by comparing these motions to those of an equivalent isolated (free) n‐alkane chain, and the same n‐alkane chain in its crystal lattice. The time‐dependent autocorrelation and (1,2)‐, (1,3)‐, (1,4)‐, and (1,5)‐cross‐correlation functions were constructed to analyze the torsion angle motions. It was found that the torsion angle motions of the DMPC lipid monolayer aliphatic chains are intermediate to those of the free n‐alkane chain and the same n‐alkane chain in its crystal lattice, particularly for short correlation times. The torsion angle motions of the aliphatic chains of DMPC are also found to be essentially independent of the charge state on the head group. The linear aliphatic chains of a DMPC lipid monolayer behave most like the isolated n‐alkane chains with respect to torsion angle flexibility, even though the pairs of aliphatic chains of each DMPC are part of an ordered monolayer assembly. The aliphatic chains of the DMPC molecules in their monolayer exhibit at least two types of wave motions. One of the wave motions is the same in form, though somewhat more diffuse, as a traveling wave found in n‐alkane crystals. The other wave motion involves major torsion angle transitions, and has some characteristics of the soliton properties observed in n‐alkane crystals near their respective melt transition temperatures. © 1997 John Wiley & Sons, Inc.

Four pharmacophore recognition sites have been proposed for active thromboxane A2 (TxA2) antagonists. We have sought to define the corresponding spatial pharmacophore for these four sites by performing conformational analysis and molecular superposition studies on five known antagonists: SQ 29,548, SQ 28,668, SQ 27,427, BM 13.505, and a Merck Frosst compound. The strategy was to identify a low intramolecular-energy conformer state for each antagonist for which the relative locations and orientations of the corresponding recognition site groups were in common when all five antagonists were superimposed. The conformations used in the successful molecular superpositions were then postulated to be the active conformations of each antagonist. Since SQ 29,548 is the most potent of the five antagonists, it was considered the reference structure in the molecular superposition. A unique spatial pharmacophore was identified and may be a useful template in designing a new TxA2 antagonists.

Credit risk, Modeling automobile leases, Risk management, Nonstationary Markovian models, C, C25, G2, G32,