Drug discovery has quickly become the most enticing place to apply artificial intelligence. Billions of dollars are being invested in AI-driven “techbios.” 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 overpromising and under-delivering to the people who need new medicines.
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, and even designing newer modalities like optimized mRNA vaccines for influenza.
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