I work in drug discovery (like for real, I have a DC under my belt, not hypothetical AI protein generation blah blah) and had the opposite experience reading it. We understand so little about most drugs. Dialing out selectivity for a closely related protein was one of the most fun and eye opening experiences of my career.
Of course we've thought of all these things. But it's typically fragmented, and oftentimes out of scope. One of the hardest parts of any R&D project is honestly just doing a literature search to the point of exhaustion.
I side with you. The more you know, the more you discover what you don’t know.
Every attempt to consider the extremely complex dynamics of human biology as a pure state machine, like with Pascal, deterministic of your know all the factors, is simplification and can safely be rejected as hypotheses.
Hormons, age, sex, weight, food, aging, sun, environmental, epigenetic changes, body composition, activity level, infections, medication all play a role, even galenic.
Put it this way: even in Pascal (especially in Pascal) you generally work in source code. You don't try to read the object code, and if you do, you generally might try to decompile or disassemble it. What you don't do -unless you're desperate- is try to understand what the program is doing by means of directly reading the hexdump (let alone actually printing it out in binary!)
Now imagine someone has written a Compiler that compiles something much more sophisticated into Pascal (some 'fourth generation language' (4GL) ) . Now you'd be working in that 4GL, not in Pascal. Looking at the Pascal source code here would be less useful. Best to look at the 4GL code.
Biology is a bit like that. It's technically deterministic all the way down (until we reach quantum effects, at least). But trying to explain why Aunt Betty sneezed by looking at the orbital hybridization state of carbon atoms might be a wee bit unuseful at times. Better to just hand her a handkerchief.
(And even this rule has exceptions: Abstractions can be leaky!)
Of course we've thought of all these things. But it's typically fragmented, and oftentimes out of scope. One of the hardest parts of any R&D project is honestly just doing a literature search to the point of exhaustion.