How AI helps scientists design the next generation of medicines

The process of developing new medicines traditionally involves extensive time and significant investment, with many potential drug candidates failing to reach patients. This challenge is amplified for biologic medicines, which are complex therapies made from engineered proteins.

AI is now transforming this landscape by speeding up drug discovery and development. Companies like AstraZeneca are integrating AI into their R&D, enabling faster iteration cycles and greater productivity through computational enhancement of design, testing, and analysis phases.

AstraZeneca's approach utilizes a "build-measure-learn" loop where AI generates and prioritizes candidate molecules, predicting success probabilities. This allows scientists to focus lab resources on the most promising candidates, reducing dead ends and accelerating progress toward previously untreatable diseases.

Beyond accelerating timelines, AI is crucial for discovering entirely new classes of medicines, such as multi-targeted biologics that optimize various properties simultaneously. McKinsey estimates that generative AI could cut drug discovery timelines by up to 50%. The effectiveness of AI models relies on high-quality, diverse biological data, which companies are actively generating and refining.

AstraZeneca is establishing a "lab of the future" to integrate AI and robotic automation into a continuous discovery system. This system uses AI for predictions, robots for experiments, and instruments for data generation, creating a closed-loop feedback mechanism where data continuously refines models.

Ultimately, the goal is "de novo" design, where AI generates entirely new protein sequences with precise desired drug properties, from structure and safety to manufacturability. This ambitious vision aims for completely AI-generated biologics, bringing previously impossible treatments within reach.