Bristol Myers Squibb deploys Nvidia's most powerful AI system to accelerate drug discovery

Bristol Myers buys Nvidia's latest AI computing system for drug research

NEW YORK, July 20 (Reuters) - Bristol Myers Squibb (BMY.N), opens new tab said on Monday it is buying the latest-generation computing system from chip company Nvidia (NVDA.O), opens new tab to support its use of artificial intelligence across its drug discovery and development operations. The drugmaker said it will be the first life sciences company to buy an Nvidia DGX SuperPOD based on its Vera Rubin systems. The chipmaker unveiled its Vera Rubin architecture earlier this year as the successor to its current generation of AI computing systems. Financial terms of the Bristol Myers investment were not disclosed. It builds on a smaller ⁠SuperPOD system the drugmaker bought from Nvidia, which is around two or three generations behind Vera Rubin, BMS executives said in an interview. Pharmaceutical companies are increasingly investing in AI infrastructure to try to identify drug targets faster and improve the odds that experimental drugs succeed in clinical trials. Robert Plenge, chief research officer at Bristol Myers, said the new capabilities would allow the company to cycle through many more potential drug candidates early in the drug development cycle. "Maybe before we could do 10 and now we can do dozens," he ⁠said. Plenge also said that the company is already using AI tools to cut the time to make medicines to test in trials by 20% to 30%. That could even reach 50% in coming years, he said. He said one experimental sickle cell disease treatment currently in early clinical development by ⁠the company would likely not have been discovered if not for AI-enabled research. Greg Meyers, the company's chief digital and technology officer, said the investment was driven in part by rapidly growing computing demands ⁠as Bristol deploys larger AI models across its research organization. It uses AI in all of its small-molecule and most of its large-molecule programs. He also said the new system ⁠will be more energy efficient. "When you host these things, you have to pay an electric bill," Meyers said. "Think of it as 10 times more compute capacity per watt spent ... Electricity is not getting cheaper." Reporting by Michael Erman; editing by David Gaffen Our Standards: The Thomson Reuters Trust Principles., opens new tab

Bristol Myers Squibb Building Life Science Industry's Most Advanced AI Factory on NVIDIA Vera Rubin

The pharmaceutical company has demonstrated how AI can transform drug discovery and is now scaling AI across R&D to accelerate the discovery of next-generation medicines. Erin Davis calls it the "SuperDuperPOD." That's two things in one name: pharmaceutical giant Bristol Myers Squibb (BMS) already runs one of the largest AI clusters in life sciences, with serious results to show for it. And they're doubling down. BMS announced today it is deploying its second NVIDIA DGX SuperPOD, this one built on eight DGX Vera Rubin NVL72 systems -- the most powerful and energy-efficient AI cluster in life sciences. "Instead of equipping a small group of researchers with access to the supercomputer, we're opening it up to literally every scientist," says Davis, vice president of research business insights and technology at BMS. "No one has to wait, and no one is told they have a limit." The eight rack-scale systems, each comprising NVIDIA Vera CPUs and Rubin GPUs, deliver up to 10x the performance per megawatt of the infrastructure it replaces. It will give researchers at the global pharmaceutical giant access to a unified AI platform -- including NVIDIA BioNeMo Agent Toolkit for biological AI -- for running predictions, training models and powering agentic workflows across the full drug discovery pipeline. What Davis and other top BMS researchers are really after is what that access makes possible: faster cycles, bigger chemical spaces and a full drug discovery pipeline where researchers think about the science, not the logistics of lining up resources. The mandate, says Payal Sheth -- a scientist who spent her career inside drug discovery labs before taking on an expanded role in January as senior vice president of therapeutic discovery sciences at BMS -- is moving from "sort of this abstract position of what AI can do to actually translating that to measurable impact." BMS has operated a DGX SuperPOD for about three years, producing meaningful results. AI-enabled target identification already saves scientists weeks of manual work, freeing time to focus on the highest-value scientific decisions. BMS's team has used AI to expand its library of CELMoD compounds -- molecules engineered to selectively degrade cancer-causing proteins, with applications in blood cancer treatment and beyond. This has opened the door to new targets and new potential medicines across a wider range of diseases. AI is also applied in lead optimization stages of drug discovery using a methodology Sheth calls "Predict First," which informs experimental gating based on design predictions. "We use predictions as a way to prioritize synthesis of molecules with multi parameter optimization," she explains, "to weed out molecules that wouldn't necessarily meet the property landscape we're working towards. This ensures precious laboratory experiments are aligned with progressing molecules that have the highest probability of success. These research AI applications have significant impact on compute needs across the research organization."We're saturated," Davis says. "We're in production with some very large-scale predictions around large molecules. We're building our own foundational models, and that takes a lot of GPUs." With the new system coming, Davis already has her pitch for researchers thinking about where to do their best work: "Welcome to Limitless Compute." A computational chemist by training, Davis spent years doing the science before concluding the technology wasn't keeping up -- and that she'd rather go fix it. She spent roughly 15 years on the vendor side, building enterprise platforms at ChemAxon, Schrödinger and X-Chem. At every company, pharma was wrestling with the same bottleneck. "It's not the technology," she says. "The challenge is how to get that into the hands of actual scientists and learn from it." She knows what's at stake personally. Her father died five years ago, she says, "a very horrible death of Alzheimer's." BMS has a significant investment in brain health -- a notoriously hard area. "Even if he was still going to die," Davis says, "if there was symptom remediation along the way, it would have saved suffering for everybody in the family. Dementia is especially cruel." Davis's team is combining the existing DGX SuperPOD and the new DGX Vera Rubin NVL72-powered system into a unified environment -- a single data plane, accessible from every BMS site globally. Barriers that made the earlier system hard to reach -- site-specific restrictions left over from past acquisitions, the need for deep computational expertise -- are being replaced with AI-native tooling managed through NVIDIA Mission Control. Researchers will be able to initiate complex predictions in plain English. "The compute infrastructure is what connects all of our scientists together and ensures that our learnings are institutionalized," Sheth explains. Datasets from a program run in Lawrenceville, New Jersey, feed models that a team in San Diego, California, can draw on. The learnings "can be applied in context of any program we work on." "There's a cumulative learning loop today in drug discovery that did not exist when I first started my career," Sheth explains. "Every project was treated differently, and there were discrete sets of learnings that did not compound into any kind of intelligence framework within discovery." Today, BMS is using AI to expand that learning loop into a discovery system where every experiment, clinical readout, and partnership compounds into higher-conviction scientific decisions, faster. Agentic workflows can further enhance the architecture of R&D. "Agents don't care," Davis says. "They go all across. And that is a huge game-changer because now we can learn from decisions across the silos and across programs." "When you as a scientist can go to an army of well-vetted, fully trained virtual scientists that have BMS knowledge baked in now you're a whole team in and of yourself." Human instincts, Sheth says, aren't replaced, "they're augmented with more quantitative insights and predictions." The ability to scale that with compute, she says, "is where the excitement of the impact of AI is going to be fully realized." "You still have to have that human brain driving things," Davis adds, "still looking for caveats and gotchas, still teaching them how to utilize knowledge. But this takes up the capabilities of individual humans substantially." Davis says the new system has a plan already mapped to it: a detailed allocation across modalities, from small and large molecule design to clinical applications to digital twins. "We didn't just buy this to have the biggest compute," she says. "The SuperDuperPOD is basically at every node along the way." When BMS Chief Digital and Technology Officer Greg Meyers asked Davis whether she was sure she could even saturate the super-duper pod, her answer was direct. "Just give us time," she told him. Featured image credit: Bristol Myers Squibb

US pharma firm ropes in Nvidia to build sector's largest supercomputer

US pharmaceutical giant Bristol Myers Squibb (BMS) has teamed up with chip maker NVIDIA to build the sector's largest AI supercomputer. The computing cluster is expected to be online by January 2027 and will be hosted by data center company Equinix under a co-location agreement. The wave of artificial intelligence that is sweeping companies across the globe isn't leaving out the pharmaceutical sector either. Although it is not the large language models (LLMs) that are helping accelerate drug discovery, the high-performance compute that powers these models is helping pharmaceutical companies build their own AI. Top leadership at these companies is now convinced that computing power is as critical to the drug discovery process as the wet laboratory. However, unlike tech companies in the Silicon Valley, which went all in with rented compute and are now paying full price for their token usage, big pharma companies took a different route and decided to own their compute instead. At a time when NVIDIA's processors are excessively in demand and seeing regular performance upgrades, the decision to buy the compute instead of renting may not seem wise. However, renting compute isn't that straightforward either. When the BMS team did the math, they realized that even when reserving capacity for compute meant that they were getting access to chips that were three to four generations old. Moreover, the cost of renting was much higher than that of purchasing the chips from NVIDIA outright. This seems to be the line of thinking at other pharma giants like Eli Lilly and Roche as well, who bought their compute in October 2025 and March 2026, respectively. BMS has worked closely with NVIDIA for three years when it installed the DGX SuperPOD for its research and development activities. While Eli Lilly compared the buy decision to having access to a 'near infinite' number of tokens. BMS is much more conservative in its way of thinking and is just optimizing its resources and isn't shy about renting compute in the future. The larger benefit of buying the compute is that the company's proprietary scientific data and intellectual property remain on the compute is owns. The AI Supercomputer that BMS is working to build consists of an NVIDIA DGX SuperPOD built with DGX Vera Rubin NVL72 systems making it the most powerful single-owned NVIDIA infrastructure available within the life science industry. The system delivers 10x the performance per megawatt of the infrastructure that it is replacing while also giving BMS researchers access to NVIDIA's BioNeMo Agent Toolkit, which can run predictions, train AI models, and power workflows across the drug discovery pipeline. "BMS has made a deliberate bet on AI, and we are beginning to see it pay off in our pipeline and operations," said Greg Meyers, Chief Digital and Technology Officer at Bristol Myers Squibb in a press release. "We're committed to translating AI into real outcomes for patients, which requires infrastructure built to match that ambition. Expanding our compute capabilities with NVIDIA gives our researchers and teams across the business the scale they need to keep BMS at the leading edge of what AI can do for drug discovery and development."

Bristol Myers buys Nvidia's latest AI computing system for drug research

Bristol Myers Squibb is buying Nvidia's latest AI computing system. This new system will support artificial intelligence in drug discovery and development. The company expects to cycle through many more potential drug candidates faster. AI tools are already cutting drug testing times by twenty to thirty percent. This investment drives rapidly growing computing demands across their research organization. Bristol Myers Squibb said on Monday it is buying the latest-generation computing system from chip company Nvidia to support its use of artificial intelligence across its drug discovery and development operations. The drugmaker said it will be the first life sciences company to buy an Nvidia DGX SuperPOD based on its Vera Rubin systems. The chipmaker ⁠unveiled its ⁠Vera Rubin architecture earlier this year as the successor to its current generation of AI computing systems. Financial terms of the Bristol Myers investment were not disclosed. It builds on a smaller SuperPOD system the drugmaker bought from Nvidia, which is around two or three generations behind Vera Rubin, BMS executives said in an interview. Pharmaceutical companies are increasingly investing in AI infrastructure to try to identify ⁠drug targets faster and improve the odds that experimental drugs succeed in clinical trials. Robert Plenge, chief research officer at Bristol Myers, said the ⁠new capabilities would allow the company to cycle through many more potential drug candidates early in the drug development cycle. "Maybe before we could do 10 and now we can do dozens," he said. Plenge also said that the company is already using AI tools to cut the time to make medicines to test in trials by 20% to 30%. That could even reach 50% in coming years, he said. He said one experimental sickle cell disease treatment currently in early clinical development by the company would likely not have been discovered if not for AI-enabled research. Greg Meyers, the company's ⁠chief digital and technology officer, said the investment was driven in part by rapidly growing computing demands as Bristol deploys larger AI models across its research organization. It uses AI in all of its small-molecule and most of its large-molecule programs. He also said the new system will be more energy efficient. "When you host these things, you have to pay an electric bill," Meyers said. "Think of it as 10 times more compute capacity per watt spent ... Electricity is not getting cheaper."

Bristol Myers Squibb Supercharges Drug Discovery With NVIDIA AI - Bristol-Myers Squibb (NYSE:BMY)

The strategic deployment establishes the most robust and energy-efficient single-owned artificial intelligence infrastructure currently operating within the life sciences sector. The move builds on the company's broader AI strategy. In May, Bristol Myers signed a strategic agreement with Anthropic to deploy AI across its operations. The partnership is designed to accelerate drug discovery and development by giving more than 30,000 employees access to institutional knowledge through Anthropic's AI models. AI Infrastructure Boost The new Vera Rubin architecture marks a significant increase in computing power for Bristol Myers' research teams. According to the company, the platform delivers up to 10 times more performance per megawatt than the previous generation. That improvement allows researchers to run larger and more complex AI models while improving energy efficiency. The deployment also extends a collaboration between Bristol Myers and NVIDIA that has spanned nearly three years. The enhanced computing capacity will support research across several therapeutic areas, including oncology, cardiovascular disease, immunology, hematology and neuroscience. Accelerating Drug Discovery Using the new computing cluster and NVIDIA's BioNeMo biological AI platform, Bristol Myers plans to train next-generation foundation models on decades of proprietary research data. The company has also adopted a "Predict First" approach, in which AI-generated predictions help shape experiments before laboratory work begins. Bristol Myers said AI now supports the design of every small-molecule drug program and most large-molecule development projects. The company expects the technology to improve decision-making from target identification through clinical proof of concept. BMY Price Action: Bristol-Myers Squibb shares were up 0.30% at $60.92 at the time of publication on Monday. The stock is approaching its 52-week high of $62.88, according to Benzinga Pro data. Image via Shutterstock Market News and Data brought to you by Benzinga APIs To add Benzinga News as your preferred source on Google, click here.

Bristol Myers buys Nvidia's latest AI computing system for drug research

NEW YORK, July 20 - Bristol Myers Squibb said on Monday it is buying the latest-generation computing system from chip company Nvidia to support its use of artificial intelligence across its drug discovery and development operations. The drugmaker said it will be the first life sciences company to buy an Nvidia DGX SuperPOD based on its Vera Rubin systems. The chipmaker unveiled its Vera Rubin architecture earlier this year as the successor to its current generation of AI computing systems. Financial terms of the Bristol Myers investment were not disclosed. It builds on a smaller SuperPOD system the drugmaker bought from Nvidia, which is around two or three generations behind Vera Rubin, BMS executives said in an interview. Pharmaceutical companies are increasingly investing in AI infrastructure to try to identify drug targets faster and improve the odds that experimental drugs succeed in clinical trials. Robert Plenge, chief research officer at Bristol Myers, said the new capabilities would allow the company to cycle through many more potential drug candidates early in the drug development cycle. "Maybe before we could do 10 and now we can do dozens," he said. Plenge also said that the company is already using AI tools to cut the time to make medicines to test in trials by 20 per cent to 30 per cent. That could even reach 50 per cent in coming years, he said. He said one experimental sickle cell disease treatment currently in early clinical development by the company would likely not have been discovered if not for AI-enabled research. Greg Meyers, the company's chief digital and technology officer, said the investment was driven in part by rapidly growing computing demands as Bristol deploys larger AI models across its research organization. It uses AI in all of its small-molecule and most of its large-molecule programs. He also said the new system will be more energy efficient. "When you host these things, you have to pay an electric bill," Meyers said. "Think of it as 10 times more compute capacity per watt spent ... Electricity is not getting cheaper."

Bristol Myers Squibb to Advance AI Infrastructure in Collaboration with Nvidia

Bristol Myers Squibb said it will expand its partnership with Nvidia, advancing its artificial-intelligence infrastructure to increase both computational power and efficiency. The biopharmaceutical company on Monday said it will deploy Nvidia DGX Vera Rubin NVL72 systems across its stack. The new architecture is expected to deliver up to 10 times greater performance per megawatt than its predecessor, enabling Bristol Myers to pursue larger and more sophisticated AI workloads without increasing energy consumption. The company said the new infrastructure will help to scale its proprietary AI models, compress discovery timelines and advance its vision of what it called collaborative hybrid intelligence. This is where AI scientists work alongside researchers to pursue first- and best-in-class medicines. Bristol Myers said its latest investment builds on nearly three years of collaboration with Nvidia, as well as marks the next step in scaling the company's AI-driven scientific programs across oncology, hematology, cardiovascular, immunology and neuroscience. "BMS has made a deliberate bet on AI, and we are beginning to see it pay off in our pipeline and operations," Chief Digital and Technology Officer Greg Meyers said.