Anthropic · Claude · MIT Technology Review
How AI helps scientists design the next generation of medicines
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In partnership with AstraZeneca.
Key facts
- McKinsey estimates that generative AI, combined with other computational tools, could cut drug discovery timelines by as much as 50%
- Looking ahead AI-driven models could help design these increasingly complex, multi-specific biologics, explains Puja Sapra
- This content was produced by Insights, the custom content arm of MIT Technology Review
- AI generates or prioritizes candidate molecules computationally, predicting which designs are most likely to succeed
Summary
Designing and developing a new medicine is an expensive, failure-prone scientific challenge. Scientists explore vast quantities of possible molecules, looking for the rare few that will bind to the right target, remain stable in the human body, and be manufacturable at scale. AI-assisted design is a growing part of how biologic drug candidates are developed, and companies like AstraZeneca are actively building its engineering teams to push this further. Sapra explains that AstraZeneca’s approach follows a build-measure-learn loop. AI generates or prioritizes candidate molecules computationally, predicting which designs are most likely to succeed. Beyond accelerating timelines, AI is also being applied to the discovery of entirely new classes of medicines. The next generation of drugs can hit multiple targets simultaneously or precisely deliver therapeutic payloads to specific cells. Looking ahead AI-driven models could help design these increasingly complex, multi-specific biologics, explains Puja Sapra.