DeepMind co-founder looks to future of medical research aided by AI
Head of Google’s AI division and co-founder of DeepMind Demis Hassabis spoke about how Google is working to reimagine the process of drug discovery and how the principles of innovation once thought to be unique to a particular field can be replaced with a more generalised approach.
“Today, the research tracks and the product tracks have converged. They’re one and the same now,” he told the Hard Fork podcast.
Hassabis emphasised the role of feedback loops between AI research and application. “It’s actually really good for research to have tight feedback loops with grounded, well-designed applications, because that is the way you really understand how your models are doing,” he said. “You can have academic metrics coming out of your ears, but the real test is when millions of users use your product. Do they find it useful? Is it beneficial to the world? And you get obviously a ton of feedback that way, and then that leads to very rapid improvements in the underlying models.”
This approach has accelerated the pace of innovation within Google and paved the way for breakthroughs such as AlphaFold, whose success in predicting protein structures has vast implications for drug discovery.
Despite the optimism within the tech community, public sentiment towards AI remains mixed. A survey by the Pew Research Center revealed a predominant concern over excitement for AI’s increased usage. Hassabis believes that the key to shifting this perception lies in demonstrating the tangible benefits of AI through impactful applications like AlphaFold and its implications for medical advancements.
“I think people are always worried about change, or disruption, and clearly, AI will bring enormous change,” said Hassabis. “It’s dawning on people, but they haven’t interacted with it. What we need to do, as a field, is present concrete use cases that are clearly incredibly beneficial. The average person in the street probably doesn’t know about AlphaFold yet, what that impact will be, but they will do if that leads to AI designed drugs and cures for really terrible diseases. And I think we’re only just a few years away from that.”
With London-based drug discovery company Isomorphic Labs, Hassabis talked about plans to take the AlphaFold technologies into chemistry and biochemistry.
“We’ve just signed big deals with Big Pharma, on real drug programmes. I expect in the next couple of years we’ll have AI designed drugs in the clinic and clinical testing. That’s going to be an amazing time. That’s when people will start to really feel the benefits in their daily lives in really material and incredible ways,” he said.
Hassabis’s vision for AI is not just about technological progress but about harnessing this progress to solve some of the world’s most pressing challenges. The journey of AI from a specialised tool to a general solution applicable across various domains illustrates the tremendous potential of AI to benefit humanity.
Which goes much further than ‘just’ healthcare. “Imagine any problem in science with a huge combinatorial search base, huge numbers of possibilities, way more than you could search by brute force,” said Hassabis. “Let’s take chemistry, the space of possible compounds. Some people estimate that’s 10 to the power 50 regarding the possible compounds one could create. Intractable to do that by hand. You can build a model of chemistry that understands what’s feasible in chemistry, you could use that to do a search, but you search just a tiny fraction of the possibilities that are of the highest potential value.
“There are a lot of things in science that fit that. Finding a drug compound that has no side effects, but binds exactly to the thing that you want in it, on your protein or the bacteria, that’s an example. Finding new materials, like a room temperature superconductor, or the ultimate battery design, those are the things I’d love to turn our systems to. All of those things can be re-imagined in a way where these types of tools and methods will be very productive.”
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