AstraZeneca is using artificial intelligence to streamline the development of biologic medicines, which are therapies made from engineered proteins. The company is leveraging AI to design and test potential drug candidates more efficiently, significantly reducing the time and cost involved in bringing new treatments to market. 'Everything we do, whether it’s design, make, test, or analyze, is now computationally enhanced,' says Puja Sapra, senior vice president and head of R&D biologics engineering and oncology targeted discovery at AstraZeneca. This approach allows the company to focus lab resources on the most promising candidates, shortening the development cycle and increasing innovation.
AI is helping AstraZeneca navigate the complexities of drug design by generating and prioritizing candidate molecules computationally. Scientists then test only the top-ranked options, leading to faster iteration and the ability to target previously untreatable diseases. The company is also applying AI to discover entirely new classes of medicines that can target multiple pathways or deliver therapies precisely to specific cells. 'Drugging the undruggable is becoming a reality,' Sapra says, highlighting the potential for AI to develop medicines against targets once thought impossible to reach.
The success of AI in drug discovery depends heavily on high-quality biological data, which AstraZeneca is actively building through proprietary datasets. These include molecular structures, binding measurements, safety profiles, and manufacturing outcomes. The company is also investing in deep screening technologies to generate additional datasets needed to refine and validate AI models. AstraZeneca is constructing a 'lab of the future' in Kendall Square, Massachusetts, where AI and robotic automation will form a closed-loop discovery system, enabling continuous, data-driven experimentation. 'This will generate AI-ready data at a scale that traditional workflows cannot match,' Sapra explains.
Source: mittr
