How AI helps scientists design the next generation of medicines
Illustrates frontier AI application in life sciences R&D - background context for agencies with health, biosecurity, or innovation portfolio responsibilities.
Key points
- AstraZeneca uses AI-driven build-measure-learn loops to accelerate biologics drug candidate design and reduce dead ends.
- McKinsey estimates generative AI combined with computational tools could cut drug discovery timelines by up to 50%.
- Limited direct relevance to APS governance or policy work; primarily a commercial R&D case study.
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"How AI helps scientists design the next generation of medicines"
Source: MIT Technology Review – AI
Published: 23 July 2026
URL: https://www.technologyreview.com/2026/07/23/1140346/how-ai-helps-scientists-design-the-next-generation-of-medicines/
MIT Technology Review profiles AstraZeneca's use of AI in biologics drug discovery, describing a closed-loop system where AI generates and ranks molecular candidates, robotic systems run experiments, and resulting data is fed back into models. AstraZeneca's SVP Puja Sapra describes proprietary multimodal datasets as a key differentiator and outlines plans for a 'lab of the future' facility in Cambridge, Massachusetts. The article positions AI-assisted drug design as enabling previously 'undruggable' disease targets, with McKinsey projecting up to 50% reduction in discovery timelines.
Retrieved from SIMS, 16 September 2026.