A QSAR model accounting for "average" G-protein-coupled receptor (GPCR) binding was built from a large set of experimental standardized binding data (1939 compounds systematically tested over 40 different GPCRs) and applied to the design of a library of "GPCR-predicted" compounds. Three hundred and sixty of these compounds were randomly selected and tested in 21 GPCR binding assays. Positives were defined by their ability to inhibit by more than 70% the binding of reference compounds at 10 microM. A 5.5-fold enrichment in positives was observed when comparing the "GPCR-predicted" compounds with 600 randomly selected compounds predicted as "non-GPCR" from a general collection. The model was efficient in predicting strongest binders, since enrichment was greater for higher cutoffs. Significant enrichment was also observed for peptidic GPCRs and receptors not included to develop the QSAR model, suggesting the usefulness of the model to design ligands binding with newly identified GPCRs, including orphan ones.

An innovative approach has been developed in order to construct new GPCR focused libraries. Experimental binding data generated in house from 1939 diverse drug and druglike compounds on 40 GPCR targets were used to develop and validate a "global GPCR" QSAR model accounting for pharmacophore features related to a general GPCR-binding behavior. To this end, proprietary 3-D descriptors representing pharmacophore fingerprints of the various conformers of the molecules were used to encode compound structures in a numerical form. Statistical treatment of the data was based on two different approaches, linear regression and predictive neighborhood behavior, and synergy models relying on both these two independent approaches were also developed. The best QSAR model was selected on hand of its statistical parameters (R 2 , RMS) and percentage of correctly predicted compounds on a randomly chosen validation set (20% of the compounds). A diverse GPCR library of 2,400 compounds was prepared by applying the global QSAR model on compounds already synthesized in house, as well as on virtual combinatorial compounds which were then synthesized if predicted to be potential GPCR binders by the model. The set of building blocks used to build combinatorial libraries has been enriched in original "GPCR-like" monomers, specially designed for this purpose according to medicinal chemistry know-how and literature knowledge. To validate our approach, 240 compounds (10%) of this library were randomly chosen and tested on 21 different amine and peptide GPCRs, together with 720 combinatorial compounds from an in house diversity-based hit-seeking library, as a reference. The experimental results on these 960 compounds were analyzed after pooling the compounds into those predicted as GPCR-active vs. inactive (360 and 600 compounds respectively). The average hit rate was found to be 5.5 fold higher for the GPCR predicted compounds and, furthermore, the global QSAR model was able to recognize not only "classical" templates but also original ones, allowing us to identify new GPCR chemotypes interacting with aminergic and peptidergic GPCRs.

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ExperimentalMaterials.-The %-pentane was Phillips Pure Grade 99 + per cent. pure. The diborane was analyzed by infrared absorption, and it contained 2.1 mole per cent. ether, 3.5 mole per cent. ethane and 94.4 mole per cent. diborane.Procedure .-The stainless steel cylinders were attached t o a vacuum system and 10.00 cc. of n-pentane was distilled into each one. The number of moles of diborane added to each cylinder was measured by filling a vacuum system of known volume to a given 7 and condensing the diborane in the cylinder wit a li%uid nitrogen bath, The cylinders were then warmed to 0.0 and thoroughly agitated to ensure equilibrium, and this process was repeated for each succeeding higher temperature. Pressures were read on ordinary gages as received with no special calibration.

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