I N D U S T R Y I N V E S T I G A T I O N
COULD BILLIONS OF DOLLARS IN AI FUNDING LEAD TO THE SAME NUMBER OF NEW DRUGS ?
Ashu Singhal and Sajith Wickramasekara , Co-founders of Benchling , explore how AI is transforming drug discovery . They argue that massive investments in AI-driven BioTech must be matched by overhauls in the entire pharmaceutical R & D system to realise AI ’ s full potential and deliver groundbreaking new medicines .
Drug discovery has quickly become the sexiest place to apply AI . Billions of dollars are being invested in AI-driven ‘ techbios ,’ and in an industry where nothing changes overnight , even large biopharma companies are touting AI as key to how they ’ re transforming their discovery engines .
But in the race to integrate AI into drug discovery , investing so heavily in scaling one part of the system overlooks the rest . Failing to reimagine R & D systems to handle the new speed and scale of AI-driven discovery risks over-promising and under-delivering to the people who need new medicines . and even designing newer modalities like optimised mRNA vaccines for influenza .
But the focus for AI can ’ t just be on discovery . The rest of the pharma R & D system – how drugs are developed , tested , approved and manufactured – will need to accommodate this vast increase in speed and scale .
The power of AI is being throttled by our R & D system
There ’ s no question that new AI models for drug discovery deserve serious attention . Within the next five to 10 years , AI will fundamentally change the way drugs are designed , with the potential to produce an order of magnitude more high-quality candidates against a broad range of new diseases . In the last year alone , AI has been used for identifying novel targets in areas like cardiomyopathy , generating novel antibodies ,
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