Chemistry - Why not simulate every particle properties and interactions?
Solution 1:
Forty electrons is tiny. Even if we limit ourselves to just the valence electons, cyclohexane already has 36 electrons. Anything drug-like has way more electrons that 40. For example, viagra has 178 valence electrons, and that's not necessarily a "large" drug. (Compare with vancomycin, for example.)
Even if you're dealing with things inorganic compounds, where the total number of atoms in the formula unit is small, the properties of the material don't come from a single formula unit, but come from the interaction of a large number of atoms. -- That's an example of a more general principle. The important properties for most materials you use (including drugs) don't come the molecule in isolation, but come from the interactions of the molecule with other molecules, either of the same chemical or of different chemicals. To be accurate, all of those interactions need a system with much more than 40 electrons.
The 40 electron limit comes from the implict assumption here that you're talking about quantum mechanical calculations. QM calculations are rather computationally expensive, as you have to account for all the interactions of all the electrons with each other at all positions in their delocalized superposition. There's various tricks (like DFT) which make the calculations for large numbers of electrons easier, but note that "easier" doesn't mean "easy". Even with DFT and other approaches, large systems take a lot of computer time to calculate accurately.
There are other approaches which don't suffer from the same limit as QM does, but they are able to make their gains in efficiency because they make approximations. For example, molecular mechanics approaches are able to simulate systems in the hundreds of thousands of atoms region. But they're able to do so because they don't actually calculate the position of electrons. Instead they treat the system "classically", and experimentally fit interaction potentials which approximate the underlying quantum effects. (For example, they don't exactly calculate the bond stretching potential, but instead approximate it as a harmonic one. That's "close enough" to the true bond stretching potential for the range of bond lengths typically seen in such simulations, but not 100% quantum mechanically accurate.)
There's many groups and companies which do use molecular mechanics and other similar approaches to inform their drug and material development process. The issue is that because the energetic potentials being used are only approximate the results from the simulation are also only approximate. Depending on what you're trying to simulate, the results of the simulation may or may not be accurate. As such, these simulations are treated mostly as a first step, to find potential leads/hypotheses, and then the scientists actually have to go into the lab and test the results to confirm.
Solution 2:
Aside from the computational power needed to simulate larger molecules, there is also a lack of knowledge about the exact mechanisms that some drugs could potentially use. Think for example of experiments in yeast or Escherichia coli cells, which are used to find new biochemical mechanisms that could be exploited for new drugs. Even though we already know a lot about those cells, it would be computationally very demanding to include all known proteins and mechanisms into any kind of simulation. Furthermore, even if we could do such a simulation, there would still be a whole lot of other proteins, genes and mechanisms which we don't really understand yet but which could very well provide new mechanisms that could be used for new drugs. For this reason we would still need (biological) experiments even if we had much greater computational power than we have today.
Solution 3:
Attempt exists, lots of drugs are first generated randomly, those most likely to have useful effects given structural category and simulation (simulation of molecular interactions and of known cell molecular pathways and physiological interactions) are tested, in cell lines, animals, then human. All that has costs and risks.
References about cell simulation:
- Wikipedia: Metabolic network modelling
- Skip the Humans: Drug Discovery by Simulating Cells by Adrienne Lafrance, the Atlantic, May 30, 2014
- R. Weinberg, M. Berkus, Int. J. Biomed. Comput. 1971, 2(2), 95-120. DOI: 10.1016/0020-7101(71)90025-0; Mirrored at deepblue.lib.umich.edu (pdf).