There’s a good post over at the Curious Wavefunction on the differences between drug discovery and the more rigorous sciences. I particularly liked this line:

The goal of many physicists was, and still is, to find three laws that account for at least 99% of the universe. But the situation in drug discovery is more akin to the situation in finance described by the physicist-turned-financial modeler Emanuel Derman; we drug hunters would consider ourselves lucky to find 99 laws that describe 3% of the drug discovery universe.

That’s one of the things that you get used to in this field, but when you step back, it’s remarkable: so much of what we do remains relentlessly empirical. I don’t just mean finding a hit in a screening assay. It goes all the way through the process, and the further you go, the more empirical it gets. Cell assays surprise you compared to enzyme preps, and animals are a totally different thing than cells. Human clinical trials are the ultimate in empirical data-gathering: there’s no other way to see if a drug is truly safe (or effective) in humans other than giving it to a whole big group of humans. We do all sorts of assays to avoid getting to that stage, or to feel more confident when we’re about to make it there, but there’s no substituted for actually doing it.

There’s a large point about reductionism to be made, too:

Part of the reason drug discovery can be challenging to physicists is because they are steeped in a culture of reductionism. Reductionism is the great legacy of twentieth-century physics, but while it worked spectacularly well for particle physics it doesn’t quite work for drug design. A physicist may see the human body or even a protein-drug system as a complex machine whose understandings we can completely understand once we break it down into its constituent parts. But the chemical and biological systems that drug discoverers deal with are classic examples of emergent phenomena. A network of proteins displays properties that are not obvious from the behavior of the individual proteins. . .Reductionism certainly doesn’t work in drug discovery in practice since the systems are so horrendously complicated, but it may not even work in principle.

And there we have one of the big underlying issues that needs to be faced by the hardware engineers, software programmers, and others who come in asking why we can’t be as productive as they are. There’s not a lot of algorithmic compressibility in this business. Whether they know it or not, many other scientists and engineers are living in worlds where they’re used to it being there when they need it. But you won’t find much here.

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