The 2013 Nobel Prize in Chemistry has gone to Martin Karplus of Harvard, Michael Levitt of Stanford, and Arieh Warshel of USC. This year's prize is one of those that covers a field by recognizing some of its most prominent developers, and this one (for computational methods) has been anticipated for some time. It's good to see it come along, though, since Karplus is now 83, and his name has been on the "Could easily win a Nobel" lists for some years now. (Anyone who's interpreted an NMR spectrum of an organic molecule will know him for a contribution that he's not even cited for by the Nobel committee, the relationship between coupling constants and dihedral angles).
Here's the Nobel Foundation's information on this year's subject matter, and it's a good overview, as usual. This one has to cover a lot of ground, though, because the topic is a large one. The writeup emphasizes (properly) the split between classical and quantum-mechanical approaches to chemical modeling. The former is easier to accomplish (relatively!), but the latter is much more relevant (crucial, in fact) as you get down towards the scale of individual atoms and bonds. Computationally, though, it's a beast. This year's laureates pioneered some very useful techniques to try to have it both ways.
This started to come together in the 1970s, and the methods used were products of necessity. The computing power available wouldn't let you just brute-force your way past many problems, so a lot of work had to go into figuring out where best to deploy the resources you had. What approximations could you get away with? How did you use your quantum-mechanical calculations to give you classical potentials to work with? Where should be boundaries between the two be drawn? Even with today's greater computational power these are still key questions, because molecular dynamics calculations can still eat up all the processor time you can throw at them.
That's especially true when you apply these methods to biomolecules like proteins and DNA, and one thing you'll notice about all three of the prize winners is that they went after these problems very early. That took a lot of nerve, given the resources available, but that's what distinguishes really first-rate scientists: they go after hard, important problems, and if the tools to tackle such things don't exist, they invent them. How hard these problems are can be seen by what we can (and still can't) do by computational simulations here in 2013. How does a protein fold, and how does it end up in the shape it has? What parts of it move around, and by how much? What forces drive the countless interactions between proteins and ligands, other proteins, DNA and RNA molecules, and all the rest? What can we simulate, and what can we predict?
I've said some critical things about molecular modeling over the years, but those have mostly been directed at people who oversell it or don't understand its limitations. People like Karplus, Levitt, and Warshel, though, know those limitations in great detail, and they've devoted their careers to pushing them back, year after year. Congratulations to them all!
More coverage: Curious Wavefunction and C&E News. The popular press coverage of this award will surely be even worse than usual, because not many people charged with writing the headlines are going to understand what it's about.
Addendum: for almost every Nobel awarded in the sciences, there are people that miss out due to the "three laureate" rule. This year, I'd say that it was Norman Allinger, whose work bears very much on the subject of this year's prize. Another prominent computational chemist whose name comes up in Nobel discussions is Ken Houk, whose work is directed more towards mechanisms of organic reactions, and who might well be recognized the next time computational chemistry comes around in Sweden.
Second addendum: for a very dissenting view of my "Kumbaya" take on today's news, see this comment, and scroll down for reactions to it. I think its take is worth splitting out into a post of its own shortly!