A friend on the computational/structural side of the business sent along this article from Nature Reviews Drug Discovery. The authors are looking through the Thomson database at drug targets that are the subject of active research in the industry, and comparing the ones that have structural information available to the ones that don't: enzyme targets (with high-resolution structures) and and GPCRs without it. They're trying to to see if structural data is worth enough to show up in the success rates (and thus the valuations) of the resulting projects.
Overall, the Thomson database has over a thousand projects in it from these two groups, a bit over 600 from the structure-enabled enzymes and just under 500 GPCR projects. What they found was that 70% of the projects in the GPCR category were listed as "suspended" or "discontinued", but only 44% of the enzyme projects were so listed. In order to correct for probability of success across different targets, the authors picked ten targets from each group that have led, overall, to similar numbers of launched drugs. Looking at the progress of the two groups, the structure-enabled projects are again lower in the "stopped" categories, with corresponding increases in discovery and the various clinical phases.
You have to go to the supplementary info for the targets themselves, but here they are: for the enzymes, it's DPP-IV, BCR-ABL, HER2 kinase, renin, Factor Xa, HDAC, HIV integrase, JAK2, Hep C protease, and cathepsin K. For the receptor projects, the list is endothelin A receptor, P2Y12, CXCR4, angiogensin II receptor, sphingosine-1-phosphate receptor, NK1, muscarinic M1, vasopressin V2, melatonin receptor, and adenosine A2A.
Looking over these, though, I think that the situation is more complicated than the authors have presented. For example, DPP-IV has good structural information now, but that's not how people got into the area. The cyanopyrrolidine class of inhibitors, which really jump-started the field, were made by analogy to a reported class of prolyl endopeptidase inhibitors (BOMCL 1996, p. 1163). Three years later, the most well-characterized Novartis compound in the series was being studied by classic enzymology techniques, because it still wasn't possible to say just how it was binding. But even more to the point, this is a well-trodden area now. Any DPP-IV project that's going on now is piggybacking not only on structural information, but on an awful lot of known SAR and toxicology.
And look at renin. That's been a target forever, structure or not. And it's safe to say that it wasn't lack of structural information that was holding the area back, nor was it the presence of it that got a compound finally through the clinic. You can say the same things about Factor Xa. The target was validated by naturally occurring peptides, and developed in various series by classical SAR. The X-ray structure of one of the first solid drug candidates in the area (rivaroxaban) bound to its target, came after the compound had been identified and the SAR had been optimized. Factor Xa efforts going on now also are standing on the shoulders of an awful lot of work.
In the case of histone deacetylase, the first launched drug in that category (SAHA, vorinostat) has already been identified before any sort of X-ray structure was available. Overall, that target is an interesting addition to the list, since there are actually a whole series of them, some of which have structural information and some of which don't. The big difficulty in that area is that we don't really know what the various roles of the different isoforms are, and thus how the profiles of different compounds might translate to the clinic, so I wouldn't say that structural data is helping with the rate-determining steps in the field.
On the receptor side, I also wouldn't say that it's lack of structural information that's necessarily holding things back in all of those cases, either. Take muscarinic M1 - muscarinic ligands have been known for a zillion years. That encompasses fairly selective antagonists, and hardly-selective-at-all agonists, so I'm not sure which class the authors intended. If they're talking about antagonists, then there are plenty already known. And if they're talking about agonists, I doubt that even detailed structural information would help, given the size of the native ligand (acetylcholine).
And the vasopressin V2 case is similar to some of the enzyme ones, in that there's already an approved drug in this category (tolvaptan), with several others in the same structural class chasing it. Then you have the adenosine A2A field, where long lists of agonists and antagonists have been found over the years, structure or not. The problem there has been finding a clinical use for them; all sorts of indications have been chased over the years, a problem that structural information would have not helped with in the least.
Now, it's true that there are projects in these categories where structure has helped out quite a bit, and it's also true that detailed GPCR structures would be welcome (and are slowly coming along, for that matter). I'm not denying either of those. But what does strike me is that there are so many confounding variables in this particular comparison, especially among the specific targets that are the subject of the article's featured graphic, that I just don't think that its conclusions follow.