How AI Helps Scientists Design the Next Generation of Medicines
As generative AI captures public attention, a different kind of AI is reshaping drug discovery. Machine learning models are helping to compress decade-long timelines and cracking problems that were previously unsolvable.
Artificial intelligence is significantly speeding up the lengthy and failure-prone process of designing biologic drug candidates, enabling faster iteration and the pursuit of previously untreatable disease targets. Companies like AstraZeneca are integrating AI into R&D to computationally generate and prioritize molecules, leading to shorter cycle times and increased innovation. The ultimate goal is AI-driven ‘de novo’ design, where AI generates entirely new protein sequences for drugs, though significant challenges remain in safety prediction and data standardization.
- AI is accelerating the design and development of biologic drug candidates, significantly reducing timelines and increasing productivity.
- AstraZeneca is using AI to computationally generate and prioritize molecules, allowing scientists to focus lab resources on the most promising candidates.
- AI enables the development of next-generation drugs that can target multiple pathways simultaneously or deliver payloads precisely to specific cells.
- The company is building an automated ‘lab of the future’ where AI and robotics work in a continuous discovery loop.
- High-quality, multimodal biological data is crucial for training effective AI models in drug discovery.
- The ultimate vision is ‘de novo’ design, where AI generates entirely new drug molecules from scratch, including predicting safety and manufacturability.
- Human oversight, judgment, and strategic direction remain central to the AI-driven drug discovery process.
Continue reading https://www.technologyreview.com/2026/07/23/1140346/how-ai-helps-scientists-design-the-next-generation-of-medicines
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