Computational chemistry has developed rapidly over the past 40 years, with advances in computing power being matched by breakthroughs in the methods used to model molecules and their behaviour. Such theoretical techniques come in two popular flavours; one is based on classical Newtonian physics and the other on quantum mechanics. Now, for making advances in developing both methods, and more specifically for combining these two approaches, Martin Karplus of the University of Strasbourg and Harvard University, Michael Levitt from the Stanford University School of Medicine and Arieh Warshel of the University of Southern California, have been awarded the 2013 Nobel Prize in Chemistry.

Although the award is for numerous advances over their respective careers, it will be associated mostly with the development of the computational method known as QM/MM (quantum mechanics/molecular mechanics), which combines both quantum and classical mechanics to simulate large molecules — such as proteins — with high computational efficiency. Classical techniques treat atoms and bonds like balls and springs, using force-fields to simulate their interactions. These methods are relatively simple and thus quite quick to carry out, but they are unable to model chemical reactions. For that, you need quantum-based calculations, but scaling these up to study large molecules is unfeasible because of their computational expense — they would simply take far too long.

Credit: © EPA EUROPEAN PRESSPHOTO AGENCY/ALAMY

In QM/MM, the chemically active region of the molecule — for example the active site of an enzyme — is treated quantum mechanically, and the rest of the molecule is treated classically, significantly speeding up the calculations while still describing the chemistry using a high-level of theory. Since these methods were pioneered in the 1970s, they have been applied to many multiscale problems, from drug discovery to understanding materials for solar cells. The continuing development of these computational strategies will undoubtedly lead to deeper chemical insights into bigger and more complex problems.