Novo Nordisk is expanding its use of AWS artificial intelligence tools across drug discovery, including AI agents for target identification, therapy design, and research workflows.
Under the agreement announced recently, AWS will become Novo Nordisk’s preferred cloud provider and strategic AI partner. The companies have also created a co-innovation hub at Novo Nordisk’s existing London facility, where AWS engineers and Novo Nordisk scientists will work directly with the pharmaceutical company’s data and research insights.
The London hub will bring Novo Nordisk R&D staff together with AWS engineers, AI specialists, applied scientists, and professional services teams. AWS said the arrangement is intended to reduce handoffs between computational analysis and laboratory research as potential drug candidates move through early development.
Thilde Hummel Bøgebjerg, executive vice president of Enterprise IT & Quality at Novo Nordisk, said the partnership combines AI technology with the company’s scientific expertise.
“AI has the potential to transform how we discover, develop and deliver medicines, but real impact comes from combining advanced technology with deep scientific expertise and a clear focus on patients,” Bøgebjerg said.
The companies said the hub is intended to shorten the path from identifying a drug target to a first human dose. The partnership will combine Novo Nordisk’s research in chronic diseases with AWS cloud infrastructure, AI services, and life sciences technologies.
AI enters more research workflows
Novo Nordisk already uses AWS technology across internal AI workloads. According to an AWS case study, more than 25,000 Novo Nordisk employees have used a generative AI platform built on Amazon Bedrock to create chatbots covering more than 2,500 use cases.
Those applications include information retrieval and document drafting across non-regulated processes. One of the larger deployments uses a collection of about 140,000 documents and processes more than 26,000 prompts each month, according to AWS.
Novo Nordisk has also applied generative AI to clinical-study documentation. AWS said a system using Anthropic’s Claude 3.5 through Amazon Bedrock reduced the time required to generate some clinical documentation by more than 90%.
According to the AWS case study, work that previously involved 40 to 50 people and could take as long as 15 weeks was reduced to minutes for a team of three. Medical professionals continue to review and validate the generated material.
The new agreement moves that use of generative AI further into drug research. One of the technologies involved is Amazon Bio Discovery, which is designed to support computational biology and drug development using AI models and agents.
AWS said Bio Discovery provides researchers with access to more than 40 biological AI models. AI agents can be used to select and coordinate models for different research tasks, while organisations can also combine AWS-hosted models with their own proprietary models in multi-step workflows.
Novo Nordisk plans to use the service for tasks including identifying potential drug targets, designing therapies, and analysing biological information. The platform can also generate and rank potential drug candidates before selected candidates are sent for laboratory synthesis and testing.
Experimental results can then be returned to the computational workflow for further analysis and model refinement. This links computational drug design with physical testing in the same research workflow.
Novo Nordisk will also use Amazon Bedrock to build AI applications that can work across clinical, genomic, and imaging datasets. According to the companies, connecting these data sources is intended to allow findings from early-stage research to inform clinical trial design.
Novo expands use of AI agents
Another part of the agreement involves Amazon Bedrock AgentCore, AWS infrastructure for deploying and operating AI agents. AWS said AgentCore allows agents to work across enterprise data, connect with existing systems, and carry out multi-step workflows.
Novo Nordisk has not detailed which AgentCore functions it plans to use in individual research workflows. The company said it intends to deploy the technology across research and operational processes.
Dan Sheeran, vice president and general manager of Healthcare and Life Sciences at AWS, said the partnership is focused on applying agentic AI across drug discovery.
“Novo Nordisk’s partnership with AWS shows what’s possible when you pair AI with deep life-sciences expertise — removing bottlenecks across drug discovery, not just studying them,” Sheeran said.
Novo Nordisk’s earlier Bedrock deployments were focused mainly on employee-facing generative AI applications. The new agreement extends its use of AWS AI tools into scientific and operational workflows linked to drug development.
AWS CEO Matt Garman said the companies intend to use the technology to shorten drug discovery timelines, process complex research datasets, and apply findings across therapeutic areas.
AWS Forward Deployed Engineers will also work directly with Novo Nordisk teams on the systems developed through the partnership. The programme embeds AWS technical staff with customers to work on AI projects, including agent-based applications.
The agreement builds on a broader relationship between Novo Nordisk and Amazon that also includes Amazon Pharmacy, Amazon Ads, and One Medical.
AWS has separately announced life sciences AI collaborations with Flagship Pioneering and Johnson & Johnson in 2026.
Novo Nordisk has also pursued AI projects outside AWS. The company has worked with OpenAI on deploying artificial intelligence across areas including drug discovery, manufacturing, and commercial operations.
It has also signed on to use Denmark’s Gefion supercomputer. The system provides computing capacity for workloads including AI model development and other data-intensive scientific research.
Novo Nordisk CEO Mike Doustdar said the AWS partnership is intended to accelerate drug discovery and expand the use of AI across the company’s work on chronic diseases.
(Photo by Google DeepMind)
See also: Why health AI interfaces must adapt to user expertise
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