Bristol Myers Squibb is purchasing an Nvidia DGX SuperPOD built on the chipmaker’s Vera Rubin architecture to support artificial intelligence use across its drug discovery and development operations.
The pharmaceutical company said it will be the first life sciences group to acquire a DGX SuperPOD based on Vera Rubin. Nvidia introduced the architecture earlier this year as the successor to its current generation of AI computing systems.
Expanding computing capacity
The new cluster will comprise eight DGX Vera Rubin NVL72 systems, with each rack-scale system combining Nvidia Vera central processing units and Rubin graphics processing units.
BMS will use the infrastructure to train proprietary models and run predictions across its research programmes. The system will support work involving compounds, proteins, and other scientific data.
Financial terms were not disclosed. The purchase expands BMS’s existing Nvidia infrastructure, which includes an older SuperPOD that company executives described as two or three generations behind Vera Rubin.
BMS has operated its existing DGX SuperPOD for about three years. The company plans to combine it with the Vera Rubin system in a shared computing environment accessible from its research sites worldwide.
The SuperPOD software stack can schedule training, prediction, and development workloads across the infrastructure. BMS said the expanded environment will give more scientists direct access to its computing resources.
Greg Meyers, BMS’s chief digital and technology officer, said computing requirements have increased as the company deploys larger AI models across its research organisation.
Erin Davis, vice president of research business insights and technology at BMS, said the existing infrastructure is operating at capacity. She attributed the demand to large-scale predictions involving large molecules and the development of internal foundation models.
Davis said the new system will not be limited to a small group of computational researchers. BMS plans to make it available across the research organisation without the waiting periods and access limits associated with its current infrastructure.
Applying AI in drug discovery
BMS said AI informs the design of every small-molecule programme and the majority of its large-molecule programmes. The technology is applied to target identification, lead optimisation, large-molecule predictions, and internal model development.
The company said AI-enabled target identification has reduced some manual research work by several weeks. Large-molecule prediction workloads are also contributing to demand for additional graphics processing capacity.
Robert Plenge, BMS’s chief research officer, said the new system will allow scientists to evaluate more potential drug candidates during the early stages of development.
“Maybe before we could do 10 and now we can do dozens,” Plenge said.
Computational screening allows researchers to assess potential compounds before selecting a smaller group for synthesis and laboratory testing.
BMS applies this approach through a method it calls “Predict First,” which uses model-generated predictions to exclude molecules that do not meet the required properties before candidates are selected for synthesis.
Payal Sheth, senior vice president of therapeutic discovery sciences at BMS, said researchers use the predictions to identify molecules with the required combination of properties.
“We use predictions as a way to prioritise synthesis of molecules with multi parameter optimisation,” Sheth said. “This ensures precious laboratory experiments are aligned with progressing molecules that have the highest probability of success.”
The method narrows the number of compounds sent for laboratory testing, allowing researchers to focus experiments on molecules that meet a programme’s predicted requirements.
BMS has also used AI to expand its library of CELMoD compounds, which are engineered to selectively degrade cancer-causing proteins. The company is studying the compounds in blood cancers and other diseases.
BMS said the modelling work helped researchers examine additional protein targets and potential compounds before deciding which candidates to pursue experimentally.
The company is also using AI tools to shorten the time required to produce medicines for clinical trials. Plenge said the process has already been reduced by between 20% and 30% and could reach 50% in the coming years.
He cited an experimental sickle cell disease treatment in early clinical development as one example of AI-supported research. Plenge said the treatment probably would not have been discovered without the company’s AI tools.
The figures refer to the time required to identify and produce candidates for clinical testing rather than their subsequent performance in trials.
The Vera Rubin system will also give researchers access to Nvidia’s BioNeMo Agent Toolkit for biological and drug-discovery applications.
BioNeMo provides tools for protein-structure prediction, molecular generation, molecular docking, sequence analysis, and genomics. It can also connect several computational tools within the same research workflow.
BMS executives said human researchers will continue to review model outputs and decide which compounds or programmes should advance.
Connecting research sites
BMS is introducing tools intended to reduce the specialist knowledge required to initiate complex computing tasks. The company said researchers will be able to start some prediction requests using natural-language instructions.
The environment will be managed through Nvidia Mission Control, whose functions include cluster provisioning, infrastructure monitoring, and workload management, according to BMS.
The unified infrastructure will allow data and model outputs generated at one site to be used by teams elsewhere. BMS said datasets from a programme in Lawrenceville, New Jersey, for example, can be incorporated into models used by researchers in San Diego.
Sheth said the shared environment is intended to retain information from experiments and research programmes across the organisation.
“The compute infrastructure is what connects all of our scientists together and ensures that our learnings are institutionalised,” Sheth said.
The two SuperPODs will operate through a common data environment, allowing teams at different sites to access shared datasets and model outputs. BMS said the environment will include information from experiments, clinical readouts, and research partnerships.
The company plans to allocate the new computing capacity across small- and large-molecule design, clinical research, and digital-twin applications. BMS did not provide details about the planned digital-twin work or the amount of capacity assigned to each area.
Meyers said the Vera Rubin system will provide more computing capacity relative to its electricity use. BMS and Nvidia said the eight-system cluster will deliver up to 10 times the performance per megawatt of the infrastructure it replaces.
“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.”
BMS did not provide a specific deployment date or identify where the new system will be hosted.
(Photo by Chidera Faustina Okeke)
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