CONSTRUCTION OF 3D-QSAR MODELS USING THE 4D-QSAR ANALYSIS FORMALISM

4D-QSAR analysis incorporates conformational and alignment freedom int o the development of 3D-QSAR models for training sets of structure-act ivity data by performing ensemble averaging, the fourth ''dimension'' The descriptors in 4D-QSAR analysis are the grid cell (spatial) occupa ncy 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 po stulated for each compound in the training set and combined with the o ptimal alignment for use in other molecular design applications includ ing other 3D-QSAR methods. The influence of the conformational entropy of each compound on its activity can be estimated. Serial use of part ial least-squares, PLS, regression and a genetic algorithm, GA, is use d to perform data reduction and identify the manifold of top 3D-QSAR m odels for a training set. The unique manifold of 3D-QSAR models is arr ived at by computing the extent of orthogonality in the residuals of e rror among the most significant 3D-QSAR models in the general GA popul ation. Receptor independent (RI) 4D-QSAR analysis has been successfull y applied to three training sets: (a) benzylpyrimidine inhibitors of d ihydrofolate reductase, (b) prostaglandin PGF(2) alpha antinidatory an alogs, and, (c) dipyridodiazepinone inhibitors of HIV-1 reverse transc riptase (RT). Two general findings from these applications are that gr id cell occupancy descriptors associated with the ''constant'' chemica l structure of an analog series can be significant in the 3D-QSAR mode ls and that there is an enormous data reduction in constructing 3D-QSA R models. The resultant SD-QSAR models can be graphically represented by plotting the significant 3D-QSAR grid cells in space along with the ir descriptor attributes.