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October 18, 2010
Palladium Couplings: You Can't Run Them All
This year's Nobel for palladium-catalyzed coupling reactions highlighted how useful these have become. But what every practicing organic chemist knows is how complicated they can be, particularly when you first couple of favorite recipes don't work. I've long thought that almost any metal-catalyzed transformation can be optimized, if you're just willing to devote enough of your life to it. But you have to have a good reason to wade into the swamp, because there sure are a lot of variables that can be tweaked. Here's a good case in point, recently published in Organic Letters. A perfectly reasonable reaction (C-H arylation of a chloropyrazole, which had been demonstrated before) was run through the statistical wringer to track down the best conditions.
They looked at 6 solvents, 10 bases, 4 catalysts, 5 ligands, and 4 additives, which would give you 7200 combinations if you ran the whole shebang. A Design of Experiments approach cut the number of actual runs down significantly, and then (fortunately) some of the variables turned out to be pretty insensitive. So this one wasn't as bad as some of them get - the ligand didn't seem to have too much effect, for example, whereas in some other Pd couplings it's crucial. (The choice of base had a much bigger effect, in case you're wondering). Their best set of conditions seems to work reasonably well across a range of possible substrates.
DoE is worth a post of its own, and that'll be a timely thing for me. After brushing up against it for years, I may finally have a use for the technique soon. For those who don't know it, it's basically a way to figure out how to most efficiently sample "experiment space", by getting the most information out of each different run. And then you use principal components analysis (or something similar) to see what the most important changes were, and how they correlate to each other. It's asking, mathematically, what a synthetic chemist wants to know about a complicated reaction recipe: what changes are responsible for most of the variation in the results, and how can I track them down by running a reasonable number of experiments? In the drug industry, process chemists think about this sort of thing a lot more than discovery chemists do, but it's worth keeping an eye out for any time the approach could be helpful.
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