AI Accelerates the Future of Medicine Design

Key points

• AstraZeneca is using AI to speed up biologic drug discovery, reducing timelines and increasing productivity.
• AI models could cut drug discovery timelines by as much as 50%, according to McKinsey.
• New AI-driven systems aim to make once untreatable disease targets reachable with innovative biologic drugs.

Designing new medicines is a costly and complex journey. Developing a drug can take many years and require a significant financial investment. Most potential treatments never even reach patients. This process is especially challenging for biologic medicines, which are therapies made from engineered proteins. These treatments tackle major diseases but involve a high level of complexity due to the vast number of molecules scientists must explore.

Now, artificial intelligence (AI) is accelerating these processes. It has become crucial in pharmaceutical research, particularly in biologic drug development. Companies like AstraZeneca are expanding their engineering teams to harness AI's potential in this field. “Everything we do, whether it’s design, make, test, or analyze, is now computationally enhanced,” says Puja Sapra, a senior vice president at AstraZeneca. By using AI, the company is able to generate and prioritize candidate molecules, predicting which designs are most likely to succeed. This approach allows scientists to focus their efforts on top candidates, leading to faster progress and the possibility of treating diseases previously considered untreatable.

How is AI changing drug design?

AI is not just speeding up the process but also enabling the discovery of entirely new classes of medicines. Traditional biologics typically target one disease pathway. However, new drugs can hit multiple targets or precisely deliver treatments to specific cells. AI-driven models help design these complex biologics by balancing factors like a drug's potency, stability, and safety. “Drugging the undruggable is becoming a reality,” says Sapra. AI is helping scientists develop medicines for targets once thought impossible to reach, offering remarkable potential benefits for patients.

What role does data play?

AI's effectiveness in drug discovery heavily relies on high-quality data. McKinsey estimates that AI and computational tools could cut drug discovery timelines by as much as 50%. At AstraZeneca, proprietary and diverse datasets provide a foundation for training AI models. These datasets include molecular structures, safety profiles, and manufacturing outcomes. Experiments, whether successful or not, generate data that informs future AI models, making data a critical differentiator in the field.

What's next for AI in medicine?

AstraZeneca is working towards a future where AI generates entirely new proteins designed from scratch, a process known as “de novo” design. This involves creating new protein sequences that fit desired drug properties and predicting how they will behave in the body. Reaching this goal requires richer datasets, robust evaluation benchmarks, and teams skilled in both machine learning and biology. One of the toughest challenges is predicting the safety of these computationally generated molecules. AstraZeneca is addressing this by using advanced cell systems and micro-scale organ models paired with AI to simulate clinical trials, generating valuable insights without traditional testing bottlenecks.

Common questions

How does AI directly affect patients?

AI speeds up drug discovery, potentially reducing costs and making treatments available faster. This means patients could access new therapies sooner.

Why is AI important in biologics?

Biologics are complex and target a wide range of diseases. AI helps streamline their development by analyzing vast amounts of data quickly, leading to innovative treatments.

Can AI replace human scientists in drug discovery?

No, AI is a tool that enhances the work of scientists. It assists in data analysis and molecule design, but human oversight is essential for strategic decisions and ethical considerations.