I remember when we were doing some early optimizing work and we had the computational chemists do some calculations on how to optimize drug binding to the receptor. "Oh, just put a methyl group here and you'll improve binding by 10x".
So we go and make the molecule and a few weeks later (it wasn't a trivial change) we go and run the assay and.... the binding is 1000x worse.
No doubt the computational methods have gotten better since this story happened, but our understanding of biology is so limited that even using AI to incorporate all known biology into a prediction isn't going to give you great results.
I do see it helping in the sense of guiding optimizing - "try and improve binding on this part of the molecule", but we're very, very far away from entering some data into a model and it spitting out a result that solves all the issues at hand.
One of the most wildly productive periods in small molecule drug discovery was back when crazy, mostly German, chemists would taste their products as part of characterizing them.
That's why, among other things, we have such wonderfully high quality data on progressive mercury poisoning.
Saccharin was discovered when Ramsen rolled a cigarette and noticed that the paper tasted sweet (there is a non-tobacco version of this for the faint of heart).
And Alexander Fleming discovered penicillin because some bread from an old sandwich did not become moldy.
Modern lab procedures preclude any such discovery today.
Penicillin is mould. Mould has been known for centuries to have special properties that resist infection, but no scientific evidence to back it up.
The bacteria culture was accidentally left open during a vacation and some mold started growing on it. When they returned, it had killed off the bacteria. Fleming isolated the mould (penicillin) and had a provable antibiotic.
Lab errors still happen, though, because they are still run by humans.
You are right, but doing the kind of work proposed in the essay is how it very slowly becomes better understood and more predictive. Obviously there won't ever be the one breakthrough that solves everything, and it's most likely that the vast majority of such attempts fail, but just throwing up our hands and saying "that's just how it works!" is how you guarantee it never changes. And this is coming from someone who works in an perhaps an even less predictive and more empirical field.
Are you implying that people haven’t been and aren’t working hard to make it more predictive? This essay gives the impression that the author has just dipped their toe into the field.
Everybody has to start somewhere but it helps to have some depth of understanding before proclaiming others as idiots (seems to be a common passtime of startups these days).
When I got into pharma I could easily see a dozen obvious ways to speed things up and/or add therapeutic value. Fortunately I kept my mouth shut and learned that all but one had been tried. That one that made us unique was more of a social practice.
I am implying nothing. I don't work in the field, and, as you say, have no idea what is or isn't being done. If, as you suggest, the kind of work being suggested by the author is being worked on, that probably would have been a more useful comment than the one you made, which didn't imply anything of the sort and seemed pretty defeatist and just "that's the way it is", which is what prompted my reply.
That's why drugs go for clinical trials, because of ceteris paribus: researchers can't figure out the conditions under which cetera are paria. In many human situations, just instrumentalism (a philosophical stance) would do, without even understanding the underlying reality.
It’s shocking how many fail in phase 3. You don’t go into an eye-wateringly expensive phase 3 unless it’s blindingly obvious from phase 2 (dose-ranging trial) that you have a winner. Which phase 2 you don’t embark on unless you have good safety data from phase 1 (often a combined phase 1/2 if you can swing it so you some belief it is efficacious in humans).
I once had a board member with 26 approved drugs from his long career at Roche. He told me “you don’t want to know how many failed after hundreds of millions had been spent. You’d be too discouraged.”
Look are Loewe’s recent column in Science: Target-based drug programs seem like an obviously sensible approach but in practice have not been fruitful: https://www.science.org/content/blog-post/target-based-drug-...
Biology is still art and luck.
Note: I’m a former small molecule pharma developer myself.