Bristol Myers Squibb acquires Nvidia AI platform to accelerate drug discovery

Bristol Myers Squibb has acquired an Nvidia DGX SuperPOD powered by the Vera Rubin architecture to accelerate artificial intelligence applications in its drug discovery and development pipeline.

The pharmaceutical giant will be the first life sciences organization to deploy a DGX SuperPOD based on Vera Rubin, Nvidia’s latest AI computing architecture unveiled earlier this year as the successor to previous generations.

Expanding computing capacity

The new cluster consists of eight DGX Vera Rubin NVL72 systems, with each rack-scale unit integrating Nvidia’s Vera central processing units and Rubin graphics processing units.

BMS will leverage this infrastructure to train proprietary models and execute predictions across its research initiatives, supporting work with compounds, proteins, and other scientific datasets.

Financial details of the transaction remain undisclosed. The acquisition augments BMS’s existing Nvidia infrastructure, which includes an older SuperPOD that executives noted is two or three generations behind Vera Rubin.

BMS has operated its current DGX SuperPOD for approximately three years. The company intends to integrate it with the new Vera Rubin system into a unified computing environment accessible from its global research sites.

The SuperPOD software stack will manage training, prediction, and development workloads across the infrastructure. According to BMS, this expanded setup will grant broader direct access to computing resources for more scientists.

Greg Meyers, BMS’s chief digital and technology officer, stated that computing demands have surged as the company deploys larger AI models throughout its research organization.

Erin Davis, vice president of research business insights and technology at BMS, noted that existing infrastructure is operating at full capacity. She attributed this demand to large-scale predictions involving complex molecules and the development of internal foundation models.

Davis emphasized that the new system will not be restricted to a small group of computational researchers. BMS plans to make it available across the entire research organization, eliminating the wait times and access restrictions of the current setup.

Applying AI in drug discovery

BMS reported that AI now informs the design of every small-molecule program and the majority of its large-molecule programs. The technology is applied to target identification, lead optimization, large-molecule predictions, and internal model development.

The company stated that AI-enabled target identification has reduced manual research efforts by several weeks. Large-molecule prediction workloads are also driving the need for additional graphics processing capacity.

Robert Plenge, BMS’s chief research officer, said the new system will enable scientists to evaluate more potential drug candidates during early development stages.

“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 subset for synthesis and laboratory testing.

BMS employs this approach through a method called “Predict First,” which uses model-generated predictions to exclude molecules that fail to meet required properties before candidates are selected for synthesis.

Payal Sheth, senior vice president of therapeutic discovery sciences at BMS, said researchers use these predictions to identify molecules with the desired 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.”

This method reduces the number of compounds sent for laboratory testing, allowing researchers to focus experiments on molecules that meet a program’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 these compounds in blood cancers and other diseases.

BMS stated that modeling 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 an example of AI-supported research. Plenge said the treatment likely would not have been discovered without the company’s AI tools.

These 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 emphasized that human researchers will continue to review model outputs and decide which compounds or programs should advance.

Connecting research sites

BMS is introducing tools designed 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, which handles 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 noted that datasets from a program 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 programs across the organization.

“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 stated that 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 stated that 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.

See also: US public health agencies to test OpenAI and Anthropic AI models

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