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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.

Recently some of us made intrinsic aqueous solubility measurements for 132 structurally diverse drugs and biologically significant molecules. We then issued, in conjunction with this Journal, an open prediction challenge in a paper where we reported the intrinsic solubilities for 100 of these compounds as a training set and listed the structures of the remaining 32 compounds which formed the prediction set. 1 This solubility challenge data were also made available online including a link on this Journal's Web site. The formal Solubility Challenge was held from approximately July 15, 2008 through September 15, 2008 although late and/or changed entries were accepted until the end of September. More than 100 entries to the Solubility Challenge have been received. In several cases multiple entries came from the same person or group. In addition, more than 5% of the prediction sheet entries were incomplete in that predictions were not reported for all 32 compounds of the prediction set. These incomplete entries were not included in the overall findings given here, but the submitted prediction sheets, like those of all other entries, were scored and returned to the contestants along with a copy of the findings of this challenge. Overall, 99 completed entries were scored and are reported here.Before presenting some of the findings from this challenge, it is important to reiterate what was stated at the outset of the challenge. The goal of this Solubility Challenge is not to identify a 'winner' but rather to adVance our general understanding of how to better perform aqueous solubility estimations. The findings of this challenge also proVide us a perspectiVe on the current state of predictiVe capabilities.The prediction set of compounds and their measured intrinsic solubilities are given in Table 1. Some qualifiers had to be applied to the prediction set to fairly and unambiguously score the contestants' data. Two of the prediction set compounds exhibit polymorph solubility behavior, and the solubility of each polymorph has been

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A methodology termed membrane-interaction QSAR (MI-QSAR) analysis has been developed in order to predict the behavior of organic compounds interacting with the phospholipid-rich regions of biological membranes. One important application of MI-QSAR analysis is to estimate ADME properties including the transport of organic solutes through biological membranes as a computational approach to forecasting drug intestinal absorption. A training set of 30 structurally diverse drugs, whose permeability coefficients across the cellular membranes of Caco-2 cells were measured, was used to construct significant MI-QSAR models of Caco-2 cell permeation. Cellular permeation is found to depend primarily upon aqueous solvation free energy (solubility) of the drug, the extent of drug interaction with a model phospholipid (DMPC) monolayer, and the conformational flexibility of the solute within the model membrane. A test set of eight drugs was used to evaluate the predictivity of the MI-QSAR models. The permeation coefficients of the test set compounds were predicted with the same accuracy as the compounds of the training set.