This month’s announcement that Alphabet’s Isomorphic Labs, led by the talented and charismatic Deepmind co-founder Demis Hassabis, had inked discovery collaboration deals with both Lilly and Novartis was just the latest indication that AI-enabled pharma R&D was ready for the big time. Billions of dollars have been invested in companies promising to “revolutionise pharma R&D” through the application of artificial intelligence – but it is now clear that global pharma companies, and not just tech bro’s, are buying into the claims.
And there is no doubt that there is plenty of room for improvement. Both the cost and the time required for each new drug approval have increased exponentially for decades (aptly tagged ‘Eroom’s Law’, being the inverse of price drops and speed gains in the semiconductor industry). Much of the eye-watering cost in getting new drugs approved results from the high failure rates (since their cost must be recouped from the winners to sustain economically viable R&D), which also negatively impact the speed of progress. Ergo anything that can improve success rates would be hugely valuable.
Step forward artificial intelligence. After decades slowly developing in the shadows, featuring more often in science fiction novels than television news programs, the arrival of large language models, and ChatGPT in particular, thrust artificial intelligence into the national consciousness. What is more it provided real-world evidence of its amazing powers accessible to anyone with a browser. It seems like a no-brainer that improved intelligence of the artificial sort might do better than humans and unlock rapid advancement and untold riches. It came as no surprise, therefore, that billions have been invested (or at least spent) on the application of these new approaches to drug discovery and development.
There is, of course, no evidence of whether the claims made for AI in discovery and development are hype or reality. With close to a decade between concept and approval of a new medicine, it is still too soon to know whether there has been any impact at all from introducing AI into various parts of the process. Even when time has elapsed, such evidence will be hard to obtain: many approvals are for more me-too drugs against a validated target, or simple reformulations and line-extensions, making anything but a step-change in R&D efficiency all but impossible to identify.
So what about the theoretical basis for the claim that AI can “revolutionise” drug discovery and development? What happens if we look beyond the most superficial truism that AI can solve lots of problems so why not this one?
To address that, we …
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