AI in Pharma: almost half a century old, news.
- BC Regulatory Blog
- Nov 1, 2024
- 2 min read
Updated: Jul 10
AI didn’t crash into pharma. It drifted in, slowly, quietly, and no one looked twice.
For most, AI was something for sci-fi films, tech expos, or obscure research papers.
Not for labs. Not for chemists in white coats.
Certainly not for the pill in your drawer.
But here’s the twist: AI didn’t just arrive in pharma.
It’s been part of the story for nearly 50 years.
Today, it’s helping discover drugs in 48 hours that once took 48 months.
Yet most people still think AI in pharma started with ChatGPT.
Before the Buzz: The 1980s AI Revolution Nobody Noticed
Go back to the early 1980s.
Before smartphones. Before Cloud anything.
Pharma researchers were already experimenting with expert systems — early rule-based AI helping predict how molecules might behave.
It wasn’t glamorous. It didn’t talk.
It ran on logic trees and best guesses.
By the ’90s and 2000s AI was hiding inside bioinformatics.
Algorithms mapping chemical structures, running toxicology models, helping decide which compounds might work.
All behind the curtain.
All uncelebrated.
This wasn’t a breakthrough.
It was a slow layering of knowledge, of code, of curiosity.
The Turning Point: When AI Found Its Voice
The 2010s changed the tempo.
Big data became bigger.
Cloud infrastructure has matured.
And deep learning finally gave AI a voice worth listening to.
Suddenly, AI could:
Predict protein folding (AlphaFold)
Design molecules (AtomNet, 2015)
Reposition existing drugs mid-pandemic (BenevolentAI, Baricitinib, 2020)
Accelerate anti-ageing research (Insilico Medicine)
What used to take years now takes weeks.
The process transitioned from benchwork to browser.
Today’s Reality: AI as Laboratory Infrastructure
AI isn’t the intern anymore.
It’s the lab.
Across the pipeline:
Target discovery pulls from genomic, proteomic, and clinical data
Drug design happens in silico — no pipettes needed
Clinical trials use AI to stratify patients and simulate outcomes
Manufacturing runs on predictive models to cut waste and time
The partnerships tell the story:
Sanofi × Exscientia — $100M upfront, up to $5.2B total
Novartis × PathAI — AI-powered pathology
Roche × Flatiron Health — $1.9B acquisition
They’re not experimenting.
They’re investing.
The Stealth Integration: Why Nobody Saw It Coming
Because AI in pharma didn’t look like a robot.
It looked like:
a spreadsheet
a pattern
a gut feeling backed by a thousand data points
It became the quietest colleague in the room —
one that never slept, never blinked, and never needed a coffee.
Until suddenly, it was discovering drugs.
Designing them.
Helping save lives.
What’s Next: The Autonomous Future
We’re not at the peak.
Not even close.
The next wave is coming:
Fully autonomous discovery pipelines
Digital twins of individual patients
Closed-loop R&D where AI doesn’t just suggest but runs the cycle, end to end
So no, AI didn’t disrupt pharma.
It embedded.
It adapted.
And now, it’s evolving faster than the industry it supports.
The Real Story
AI didn’t arrive in 2022.
It’s just that the world finally turned its head.
What other industries has AI been quietly revolutionising while we weren’t looking?
Share your thoughts in the comments.
I’m curious which “overnight success” stories have been decades in the making.
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