This past week, computing giant NVIDIA announced a new partnership with Novo Nordisk and the Danish Centre for AI Innovation (DCAI) to quicken drug discovery through the use of custom AI models and DCAI’s Gefion supercomputer, also powered by NVIDIA technology.
Described as an “AI factory” in the news release, the companies’ aim for the platform is to allow Novo Nordisk researchers to progress on several research programs related to drug discovery. This partnership is the latest in a series of initiatives showing that corporations and research institutes alike are betting on AI becoming an integral part of drug development pipelines.
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For lab managers, this collaboration offers further evidence that AI readiness may soon play a pivotal role in keeping a lab competitive.
The goals of the collaboration
This Novo Nordisk and NVIDIA partnership aims for scientists to focus on multiple AI research programs targeting different areas of the drug discovery process, including:
- Running single-cell AI models to predict responses to drug candidates,
- Building new molecules with drug-like properties,
- Processing Novo Nordisk’s scientific literature to train large language models on biomedical research data, “enabling researchers to uncover correlations between genes, proteins, and diseases” through the use of natural-language queries
“By coupling NVIDIA’s accelerated computing platform and expertise with Novo’s deep expertise in life sciences research and development,” Mishal Patel, senior vice president, AI and digital innovation at Novo Nordisk, said in a news release, “we aim to build custom models that will aid our scientists in developing new medicines faster and more efficiently.”
Why AI?
The primary benefit of AI-driven drug discovery is reducing the costs and timelines required of traditional discovery processes. An AI model like AlphaFold or Boltz can predict molecular interactions far faster than established methods that rely on brute-force screening.
Additionally, according to Christian Olsen, associate vice president, industry principal, biologics at Dotmatics, “AI contributes [to higher success rates of drug discovery] through refining candidate selection, enabling deeper insight into molecular formats and mechanisms, and facilitating earlier detection of off-target effects.”
By minimizing trial-and-error, Olsen told Lab Manager, AI allows researchers to identify dead ends earlier and allocate more resources on the most promising drug candidates.
AI readiness and the future of pharma research
AI-driven drug discovery projects are increasingly common. In January, NVIDIA partnered with Illumina to enhance Illumina’s multiomics analysis platform with AI also with the goal of quickening drug discovery. Similarly, Alphabet’s Isomorphic Labs recently raised $600 million with the goal of accelerating drug discovery with AI through the use of their flagship tool AlphaFold.
For lab managers, this trend underscores the importance of preparing their labs to be AI-ready. While most labs won’t have access to an independent supercomputer, AI tools are steadily becoming more accessible through cloud platforms, ELNs, and LIMS integrations. Labs that invest in digital infrastructure now—such as structured data capture, interoperable systems, and staff training—will be best positioned to adopt AI-powered tools as they become more mainstream.
