Man, am I getting all kinds of comments (here and by e-mail) about my views on modeling, QSAR, and the like. I thought it might be helpful for me to clarify my position on these things.
First off, structure. It's a valuable thing to have. My comments on the recent Nature Reviews Drug Discovery article were not meant to suggest otherwise, just to point out that the set of examples the authors picked to make this point was (in my view) flawed. It's actually surprisingly hard to come up with good comparison sets that isolate the effect of having structural information on the success of drug discovery projects. There are too many variables, and too many of them aren't independent. But just because a question (does having structural information help, overall?) is hard to answer doesn't mean that the answer is "no".
As an aside, since I've talked here about my admiration for fragment-based approaches, my own opinion should have been pretty clear already. Doing fragment-based drug discovery without good structural information looks to be very hard indeed.
Now, that said, there's structure and there's structure. Like every other tool in our kit, this one can be used well or used poorly. I think that fragment projects (to pick one example) get a lot of bang-for-the-buck out of structural data, and at the opposite end of the scale are those projects that only get good X-ray data after they've sent their compound to the clinic. No, wait, let me take that back. In those cases, the structure did no good, but it also did no harm. At the true opposite end of the scale are the projects where having structural data actually slowed things down. That's not frequent, but it does happen. Sometimes you have solid data, but for one reason or another the X-ray isn't corresponding to what's happening in real life. And sometimes this kicks in when medicinal chemists try to make too much out of less compelling structural data, just because it's all they have.
Now for in silico techniques. I have a similar attitude towards modeling of all kinds, but at one further remove than physical structure data. That is, I think it can be used well or used poorly, but I think that (for various reasons) the chances of using it poorly are somewhat increased. One reason is that modeling can be very hard to do well, naturally. And at the same time, tools with which to model conformations, docking, and so on are pretty widely available, which leads to a fair amount of work from people who really don't know what they're doing. Another reason is that the validity of any given model is of limited scope, as is the case with any mental construct that we have about what our molecules are doing, whether we used a software package or waved our hands around in the air. The software-package version of some binding model is more likely to have a wider range of usefulness than the hand-waving one, but they'll both break down at some point as you explore a range of compounds.
The key then is to figure out as quickly as possible if the project you're working on would be enhanced by modeling, or if such modeling would be merely ornamental, or even harmful. And that's not always easy to do. Any reasonable model is going to need a few iterations to get up to speed, generally requiring some specific compounds to be made by the chemists, and if you're running a project, you have to decide how much effort is worth spending to do that. You don't want to end up endlessly trying to refine the model, but at the same time, that model could turn out to be very useful after a few more turns of the crank. Which way to go? The same decisions apply, naturally, to the folks standing in front of the hoods, even without any modeling. How many more compounds are worth making in a given series? Would that effort be better used somewhere else? These calls are why we're paid the approximation of the big bucks.
So, while I don't think that modeling is an invariable boon to a project, neither do I think it's a waste of time. Sometimes it's one, and sometimes it's the other, and most of the time it's a mix of each - just like ideas at the bench. When modeling works, it can be a real help in sending the chemists down a productive path. On the other hand, you can certainly run a whole project with no modeling at all, just good old-fashioned analoging from the labs. It's the job of modelers to make the first possibility more likely and more attractive, and the job of the chemists and project managers to be open to that (and to be ready to emphasize or de-emphasize things as they develop).
This point of view seems reasonable to me (which is why I hold it!) But it also exposes me to complaints from people at both ends of the spectrum. I'm a lot more skeptical of in silico approaches than are many true believers, but I don't want to make the mistake of dismissing them outright.