If anything, fast quantum calculations would make an even bigger difference in materials science than in drug discovery. That's because druglike molecules have been studied a lot, so classical approximations (though limited) at least exist.
"Simulating Physics with Computers", International Journal of Theoretical Physics, volume 21, 1982, p. 467-488, at p. 486 (final words) [0]
The limiting step in being able to manufacture any small organic molecule from raw materials is algorithmic. Imagine a molecule printer that can compile a set of synthesis steps for an arbitrary molecule via simulation. It's not as far-fetched if we can simulate hypothetical reactions easily: http://www.nature.com/news/organic-synthesis-the-robo-chemis...
The other parts of what you're saying are true but don't depend on what happens in quantum computing, they're just part of the for-profit drug industry. There are government and philanthropic drug hunters today who are doing good work, and if they had quantum drug discovery, they would be able to do even more.
The new computing model was later found to be generally useful and applicable to things like climate modeling too.
The simplistic intuition is that there are a lot of exact algorithms which are exponential, so we use clumsy polynomial approximations instead. A quantum computer makes many categories of exponential algorithms polynomial and therefore computable if a big and fast enough quantum computer can be built.
Anyone with more expertise care to point me at a relevant paper?
https://medium.com/@_NicT_/quantum-machine-learning-c5ab31f6...
With respect to biological simulations, researchers at IBM were able to implement artificial life algorithms on a quantum computer [0]. They discuss some potential advantages to simulating life on a quantum computer versus a classical one.
Researchers have also been trying to figure out if quantum computers can be used for weather predictions [1].
[0]: https://arxiv.org/abs/1711.09442
[1]: https://link.springer.com/article/10.3103/S1068373917090011
Genetic algorithms are similar in some regards to simulated annealing. Apparently, D-Wave's approach is kind of like quantum simulated annealing.
https://arstechnica.com/science/2017/01/explaining-the-upsid...
Added: that is, I thought protein conformation energy was pretty well approximated with classical molecular-mechanics models, except during actual chemical reactions, and the difficulty was the sheer amount of time it takes this huge molecule to settle down to an energy minimum that's only a little below that of many alternative conformations. Is the biggest problem rather that we can't efficiently compute a consistent-enough approximation to the energy of a given conformation, so we can't distinguish the best conformation of the ones we simulated?
Are there non-classical algorithms with theoretical advantages for biological or climate simulations? Or is this just more hype-nonsense courtesy of the press office?