As a computational biologist I rather like the look of this year’s Nobel Prize for Chemistry – it’s been awarded for contributions to computational modelling, to Martin Karplus (University de Strasbourg/Harvard), Michael Levitt (Standford) and Arieh Warshel (University of Southern California).
Molecular modelling takes several forms. The twist in the work the prize has been awarded for is multi-scale modelling, in their case bridging classical (Newtonian) and quantum modelling.
Take a ball. Given the forces on the ball you can apply the ‘classical’ physics of Sir Isaac Newton (and those that furthered his work) to determine where the ball will be at a given time in the (near) future. That’s Newtonian modelling.
We can think of molecules—chemicals of several atoms or many more—as balls connected by sticks, chemical bonds.
To the right is a ball-and-stick model of a single amino acid, proline.
If you can treat atoms in a molecule as balls, you can apply Newtonian physics to them.
Atoms are joined to each other by chemical bonds. Different types of bonds have different rotational properties. Some rotate freely, others less so. So we can add rotational properties to our model.
There’s the different forces atoms have on eachother. We can simplify these to something like compressing springs that draw together or push apart atoms or groups that attract or repel eachother.
Keep going and we can built up a method to simulate the motion of molecules.