Ossibility from the continuum of conformations. Thus, using differently optimized models (e.g. obtained by slightly different ligand placements or different force field parameters), the set of identified ligands would have changed. Yet, the overall performance, with some models being able to recognize ligands and some not, would be similar. This fact might also be considered disheartening for approaches that aim to include receptor flexibility via docking to multiple conformations of a receptor and calculating the average rank of a molecule across all structures. Second, docking to GPCRs, even using “only” homology models, works well. The screen against the A1AR was successful by all criteria, with a hit rate of 21 and potent compounds with Ki values as low as 400 nM for the 2H-chromen-2-imine derivative 17. Some of the ligands also represent novel chemotypes for the A1AR, such as 17 and related, albeit only weakly active, derivatives quinazolin-4(3H)-ones (14, 22, 25) and a pyrido[2,3d]pyrimidin-4(3H)-one (26). In particular, the ligands identified with model D tend to have ECFP4 Tanimoto similarity values of less than 0.40 when compared to the 7173 AR ligands in the ChEMBL database. The reason for the relatively few genuinely novel ligands presumably lies in the bias of the library, as shown before [47]. However, the overall performance of this screen is in line with previous docking studies that identified numerous and potent GPCR ligands [9,45?9]. As was 1313429 the case here, most of these campaigns targeted a Class A GPCR that binds small organic molecules. Such receptors tend to have rather narrow, well-defined binding sites ?in contrast to the CXCR4 receptor, the only peptide-bound GPCR structure elucidated so far [50]. Smaller binding pockets make for narrower physical search spaces which is likely one of the reasons behind the tractability of these GPCRs by docking and similar approaches.Third, for receptors with high degrees of similarity, such as the ARs, selective compounds cannot be predicted solely by docking to one receptor subtype. Most of the ligands identified as A1AR hits also bound to one of the other AR subtypes, and vice versa. In fact, the screen directed toward the A1AR worked even better against the A3AR, with a hit rate of 36 and the most potent compound inhibiting with a Ki of 36 nM. This is an advantage if it is desired to discover ligands for other related GPCR subtypes within a single GSK126 site screening process. However, there is one compound, 8, which appears selective for the A1AR by the criteria used in this screen. In addition, some of the ligands were also moderately selective in binding to the A3AR, which may be due to the fact that the binding pocket of the A3AR is the most divergent one when comparing the three AR subtypes (Table S3), suggesting the relative ease of achieving A3AR selectivity. This tendency to cross over to other subtypes in the screening process can be expected from the similarity of the binding sites. It is difficult to estimate, however, to what degree the use of homology models affected the selectivity of the compounds. Bias stemming from the template used (the A2AAR) cannot be ruled out, but cannot be the only factor as evidenced by the many compounds binding to A3AR. Very likely, even computational screens employing X-ray structures result in similarly nonsubtypeselective hit compounds. However, because biochemical testing is limited to the targeted GSK2879552 chemical information subtype in most studies, this does not.Ossibility from the continuum of conformations. Thus, using differently optimized models (e.g. obtained by slightly different ligand placements or different force field parameters), the set of identified ligands would have changed. Yet, the overall performance, with some models being able to recognize ligands and some not, would be similar. This fact might also be considered disheartening for approaches that aim to include receptor flexibility via docking to multiple conformations of a receptor and calculating the average rank of a molecule across all structures. Second, docking to GPCRs, even using “only” homology models, works well. The screen against the A1AR was successful by all criteria, with a hit rate of 21 and potent compounds with Ki values as low as 400 nM for the 2H-chromen-2-imine derivative 17. Some of the ligands also represent novel chemotypes for the A1AR, such as 17 and related, albeit only weakly active, derivatives quinazolin-4(3H)-ones (14, 22, 25) and a pyrido[2,3d]pyrimidin-4(3H)-one (26). In particular, the ligands identified with model D tend to have ECFP4 Tanimoto similarity values of less than 0.40 when compared to the 7173 AR ligands in the ChEMBL database. The reason for the relatively few genuinely novel ligands presumably lies in the bias of the library, as shown before [47]. However, the overall performance of this screen is in line with previous docking studies that identified numerous and potent GPCR ligands [9,45?9]. As was 1313429 the case here, most of these campaigns targeted a Class A GPCR that binds small organic molecules. Such receptors tend to have rather narrow, well-defined binding sites ?in contrast to the CXCR4 receptor, the only peptide-bound GPCR structure elucidated so far [50]. Smaller binding pockets make for narrower physical search spaces which is likely one of the reasons behind the tractability of these GPCRs by docking and similar approaches.Third, for receptors with high degrees of similarity, such as the ARs, selective compounds cannot be predicted solely by docking to one receptor subtype. Most of the ligands identified as A1AR hits also bound to one of the other AR subtypes, and vice versa. In fact, the screen directed toward the A1AR worked even better against the A3AR, with a hit rate of 36 and the most potent compound inhibiting with a Ki of 36 nM. This is an advantage if it is desired to discover ligands for other related GPCR subtypes within a single screening process. However, there is one compound, 8, which appears selective for the A1AR by the criteria used in this screen. In addition, some of the ligands were also moderately selective in binding to the A3AR, which may be due to the fact that the binding pocket of the A3AR is the most divergent one when comparing the three AR subtypes (Table S3), suggesting the relative ease of achieving A3AR selectivity. This tendency to cross over to other subtypes in the screening process can be expected from the similarity of the binding sites. It is difficult to estimate, however, to what degree the use of homology models affected the selectivity of the compounds. Bias stemming from the template used (the A2AAR) cannot be ruled out, but cannot be the only factor as evidenced by the many compounds binding to A3AR. Very likely, even computational screens employing X-ray structures result in similarly nonsubtypeselective hit compounds. However, because biochemical testing is limited to the targeted subtype in most studies, this does not.