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The review deals with ten early discovery projects involving diverse targets where a variety of modeling techniques were used to improve affinity, selectivity, solubility, pharmacokinetic properties and a bunch of other desired druglike characteristics.
Some of the applications (filling hydrophobic pockets with small aromatic substituents or designing 'steric bumps' to get selectivity against other protein subtypes) are relatively straightforward while others (scaffold hopping, getting affinity by generalized electrostatic optimization, homology modeling) are more challenging and interesting. Here is a table displaying target type, approach and impact of the various protocols.
Most of the projects benefited from early crystallographic data and in fact make a case for getting this kind of data as early as possible, even when you have relatively weak hits (as I have found out through experience, you can get a perfectly reasonable co-crystal structure with a 10 µM hit). At the same time, crystallographic data can sometimes actually surprise and tell you where you went wrong; for instance there is an example of a tryptase inhibitor whose scaffold was redesigned and found to be favorable through modeling, only to realize from the crystal structure that the scaffold was in fact flipped through 180 degrees. That particular example illustrates that occasionally you can get the right answer through the wrong process, although knowing this fact as early as possible itself is quite useful.
Homology models pose a particular challenge for modeling; as I described in a previous post, a small change in the torsional angle of even a single residue can impact your ligand binding prediction. In this review the authors make a good case for using homology models even as crude aids. The crux of the matter is generating good hypotheses, and even crude models can help us do that. In this particular case, based on an initial lead, the model was used to predict a position to add polarity to the molecule. This led to a sulfonamide being replaced by an amide and a spiro ring system which retained potency and good properties.
The last few cases deal with ligand-based optimization in which the lack of 3D protein structural information required the use of 3D ligand overlays. The authors make another important point here: in one of their case studies they used a simple 'shape envelope' instead of detailed QSAR analysis to guide ligand design. As they point out, doing ligand overlaps for detailed QSAR analysis can be very tricky; the devil is in the molecular details and small differences in atom placement might throw you off. There have been several articles bemoaning the limitations of QSAR in recent years, so this sounds like a safe thing to do.
There are some obvious limitations to using such techniques which I am sure the authors are well aware of. Their list features hits, not misses, and many of the techniques which may have worked in these particular cases may not have worked in others. In addition, the review does not explore whether there is in fact a causal relationship between the technique used and the result obtained since other hypotheses aren't always explored. Nevertheless, it is unrealistic to expect researchers to try out every single hypotheses in a project, and what I find most useful in any case about this article is that it provides us with a checklist of things to try and conjectures to test. Science is about ideas, not answers, and as with anything else in drug discovery, if one thing fails you just hold your head high and try another.
The review concludes with a set of lessons which I think are valuable guidelines to think about in any molecular design project. The importance of the first lesson cannot be overemphasized: qualitative statements can often be more useful than quantitative analysis. I can't say this statement doesn't make me beam with pleasure. It is an antidote to those who think of chemistry as physics and expect quantitative predictions. As the case studies here demonstrate, not only are quantitative predictions often a fool's errand but in many cases they aren't even necessary.
This is a point that I think is often lost on the critics of molecular modeling. The goal of modeling is not just to make detailed predictions; it is to cull unnecessary directions of inquiry, save labor, guide researchers into previously unexplored areas of thinking and generate hypotheses that can be quickly made and tested. It is to help think about molecular design in the broadest possible way. This is value that goes far beyond being able to rank order your latest deck of hits, and it's something to keep in mind the next time an experimentalist asks you whether you can do that.
The other lessons are also worth remembering: use molecular design to shape medicinal chemistry space, employ the principle of parsimony (and Occam's razor), annotate whenever possible (even simple visualization of molecular interactions can be eye-opening), realize the domain of applicability of your techniques (occasionally by stress-testing them) and perhaps most importantly, stay close to experiment. That last part is something all good modelers should know; don't use quantum chemical torsional calculations when you can look up features in the CSD, don't use homology models when you can convince the crystallographer to get you even a low-resolution crystal structure, don't use fancy scaffold morphing software when the medicinal chemist tells you that he or she can rapidly make alternative scaffolds.
As the review concludes:
"Best practice in molecular design is best practice in all sciences: a relentless focus on clarity, simplicity and good experimental design. What is special about molecular design is the need to build solid hypotheses and to simultaneously foster creative thinking in medicinal chemistry. If we accept this, our focus may shift from the many semi-quantitative prediction tools we have to methods supporting this creative process. Further improvements in computational methods may then have less to do with science than with good software engineering and interface design. The tools are a just means to an end. Good science happens when they are appropriately employed."A means to an end indeed. Modeling is a poor master but can be a very useful servant.