In recent years, we’ve seen the world lashed by a pandemic, we’ve borne witness to rising healthcare costs, and seen how agonizingly slow drug development timelines can be. There is hope on the horizon though, and it’s emerging from the frontiers of artificial intelligence, with the potential to end all diseases within the next decade.
This might sound like something out of a sci-fi script, but it isn’t. Sir Demis Hassabis, co-founder and CEO of Google DeepMind, believes artificial intelligence is on the brink of transforming medicine as we know it. And he should know a thing or two about it, having been recently awarded the Nobel Prize in Chemistry for his work decoding protein structures using AI,
“It takes, on average, ten years and billions of dollars to develop just one drug,” Hassabis explained in an interview on CBS’ 60 Minutes. “We could maybe reduce that down to months, or even weeks.”
And he’s not alone in his optimism. Leading thinkers in biotech and AI, from startups to Big Pharma, are racing to deploy machine learning tools that can slash drug discovery timeframes, unlock novel treatments, and personalize care in ways that were once unthinkable.
Cracking the Code of Life
Until recently, mapping the structure of a single protein — crucial for understanding how diseases work — could take a PhD student half a decade. But with DeepMind’s AlphaFold, over 200 million protein structures were mapped in under a year. According to Hassabis, that’s equivalent to a billion years of PhD time compressed into twelve months.
These structures, the building blocks of biology, are now fueling the next frontier: AI-driven drug discovery. With detailed protein maps in hand, AI can predict how diseases disrupt biological processes — and how custom molecules might fix them.
“If we know the function, then we can understand what goes wrong in disease,” Hassabis said. “And we can design drugs and molecules that will bind to the right part of the surface of the protein.”
The Great AI Drug Race
The biopharma sector is already deep into what some are calling the great AI drug race. More than 75 “AI-discovered molecules” have entered clinical trials, according to an analysis by Boston Consulting Group. Many more are expected in the coming years.
Companies like Insilico Medicine and Recursion Pharmaceuticals are using AI to identify disease targets and design entirely novel molecules from scratch. One Insilico drug candidate for idiopathic pulmonary fibrosis (IPF) was discovered using generative AI — a process that took just 18 months and 79 molecule variations, compared to the industry average of four years and 500+ variations.
What’s different now is scale and precision. AI doesn’t just suggest new drug candidates — it can simulate how they’ll behave in the body, predict toxicity, and even estimate their chances of clinical success. This has the potential to dramatically reduce the 90% failure rate that has long plagued drug development.
Reimagining Scientific Discovery
According to Hassabis, the real breakthrough lies not just in speeding up drug discovery, but in redefining how science is done. In the near future, he predicts, AI will move from solving problems to posing questions — developing hypotheses, designing experiments, and even imagining new classes of medicines.
“In the next five to ten years,” he said, “we’ll have systems that are capable of coming up with new hypotheses in science on their own.”
This evolution marks the dawn of what Hassabis calls radical abundance — a world where scarcity of treatments, and even disease itself, may one day be eliminated.
Guardrails for Genius
Of course, the excitement comes with caveats. Hassabis, who also serves as an AI advisor to the UK government, is deeply aware of the ethical implications. AI’s incredible potential must be paired with equally strong safety systems to prevent misuse and ensure alignment with human values.
“Can we make sure they stay on the guardrails?” he asks, referencing the crucial challenge of governing autonomous AI systems.
Still, the overwhelming momentum in research, investment, and public interest suggests we’ve crossed a threshold. AI is no longer the mere assistant in the lab — it’s quickly becoming the principal investigator.
A World Without Disease?
From Google’s Isomorphic Labs to startups like Recursion, a new generation of AI-native biotech firms is building platforms to accelerate every stage of drug development — from molecular modelling to clinical trial prediction.
While AI won’t replace pharmaceutical scientists anytime soon, it’s already transforming how they work — from manual experimentation to machine-led imagination.
As Hassabis puts it, “AI is the ultimate tool for advancing human knowledge.” If his predictions prove correct, the defining shift in 21st-century medicine might not come from a lab bench, but from a line of code.
And with the convergence of AI capabilities, biological understanding, and global need, the question may no longer be if AI can cure disease — but when.
“I think one day, maybe we can cure all disease with the help of AI,” says Hassabis. “Maybe within the next decade. I don’t see why not.”
