Virtual screening is what many people outside the field are thinking of when they talk about the use of computational models in drug discovery. There are many other places where modeling can pitch in, but one of the dreams has always been to take a given protein target and a long list of chemical structures, hit the button, and come back to a sorted list of which ones are going to bind well. That list could be as long as "every compound in our screening deck", or "every available compound in the commercial catalogs", or "everything that our chemists can think of and draw on a whiteboard, whether it's ever been made or not". So these virtual collections can get rather large, but that's what computer power is for, right?
Despite what some people might think, we're not exactly there yet. But we're not exactly not there, either, if you know what I mean. Like much of drug discovery, it's in that awkward age. Virtual screening is certainly real, and it can be useful, but it can also waste your time if you're not careful. And that's where this paper comes in - it's a fine overview of the issue that you need to think about if you're interested in trying this technique.
For one thing, you need to decide if you're going to be taking a drug target whose structure you know pretty well and modeling a bunch of small compounds into it, or if you're taking a bunch of small molecules whose activities you know pretty well and trying to find more compounds like them. These two approaches call for some different methods, and have different potential problems. The second one, especially in the older literature, often goes under the name of QSAR, for quantitative structure-activity relationship. But as the authors point out, "virtual screening" as a name has some advantages, because many people have been burned by things labeled "QSAR" over the years. They're also being used for different purposes, which is probably a good thing:
A fundamental assumption inherent in QSAR and pharmacophore-based VS is the “similar property principle”, that is, the general observation that molecules with similar structure are likely to have similar properties. While this assumption holds true in many cases, there are many counter-examples in the field of QSAR which lead to erroneous predictions and can shake the confidence of the experimental community in the prospective utility of QSAR modeling. Interestingly, this has not yet (or not to the same extent) been the case with VS. The difference is that QSAR is typically employed to evaluate a limited number of synthetic candidates, where errors are more noticeable and costly. However, when these techniques are applied on a massive scale to screen large chemical libraries, errors are much more easily tolerated as the objective is to increase the number and diversity of hits over what would have been otherwise a random selection.
The authors extensively cover the previous literature on computational screening - successful examples, warnings of trouble, theoretical predictions both optimistic and pessimistic. It would take you quite a while to assemble this list on your own, so that by itself recommends this paper to anyone interested in the area. But they go on to codify the various pitfalls to look out for.
"Such as expecting it to work", the cynics in the audience will remark. I say that sort of thing under my breath for time to time myself - or audibly, as the case may be. But this is the sort of paper that I can really endorse, because it's a completely realistic view of what you can expect with current technology. And that comes down to "Less than you want", but still "More than you might think". You're not going to able to feed the software the complete pile of all the chemical supplier catalogs and come back to find the nanomolar leads printing out. But you can get pointers toward parts of chemical space that you wouldn't have thought about (or wouldn't have been able to physically screen).
One tricky part is that when a virtual screening effort is successful (for whatever value you assign to "success"), it can be hard to tell why, and likewise for failures. There are so many places where things can disconnect - proteins are mobile, and small molecules even more so, and accounting for these conformational ensembles is not trivial. Binding interactions are not always well understood, or well modeled. Water molecules are pesky, but can be vitally important. You might have picked inappropriate controls (positive or negative), or be weighting the various computed factors in the wrong way. Either of those will send your calculation further and further off the rails.
And so on. The paper goes into detail on these possibilities and more; I highly recommend it for anyone getting into virtual screening (or for anyone already doing it, to keep the troubleshooting guide in one handy place).