Snowflake and NVIDIA BioNeMo Agent Toolkit Power the Era of Agentic AI in Life Sciences

Executive Summary

• Snowflake and the NVIDIA BioNeMo Agent Toolkit bring together governed agentic workflows and specialized biological intelligence. By combining Snowflake’s agentic control plane harnessed with NVIDIA’s domain-specific AI models and frameworks, organizations can run complex scientific AI workflows where their data already resides.

• This collaboration can help pharmaceutical R&D teams accelerate research and development.

• The future of AI in life sciences depends on secure, scalable orchestration across infrastructure, data, reasoning and domain expertise. With Snowflake and the BioNeMo Agent Toolkit, life sciences organizations can move toward enterprise-ready agentic AI that transforms scientific discovery through trusted agentic workflows. 

The life sciences industry is at a unique inflection point. Two powerful forces are converging: the rapid expansion and growing complexity of scientific and clinical data, and the rise of frontier AI technologies that can reason, plan and execute multi-step processes.

This new agentic era moves beyond earlier, single-purpose AI tools toward adaptive systems that can perceive, evaluate, conclude and act directly within business-critical workflows. For life sciences organizations, this shift has the potential to change discovery methods, redefine clinical workflows, and streamline development timelines.

The significance of this evolution is profound. Traditionally, the pharmaceutical journey spans over a decade, with investment costs frequently exceeding $1 billion. By empowering research teams to discover novel targets, previously unattainable, operationalize sophisticated workflows, and prioritize high-value human insight, agentic AI serves to shorten development cycles and enhance strategic reasoning throughout the entire R&D continuum.

A new AI era demands a new approach

Scientific discovery has always been a data problem. Genomic sequences, protein structures, clinical outcomes, molecular interactions - the raw materials of biological insight are vast, multimodal and deeply interconnected. Historically, AI models that reason over this data have lived far from where it resides, requiring costly data movement across regulatory trust boundaries.

At Snowflake, we believe a new approach is required for successful enterprise-grade AI. One where data, AI and domain intelligence are unified, connected and harnessed to act and enable enterprises to govern, orchestrate and operationalize AI agents through a secure agentic control plane.

We are excited to share Snowflake and NVIDIA’s joint vision for bringing agentic AI to life sciences enterprises through a fully governed agentic control plane for R&D.

The BioNeMo Agent Toolkit provides a suite of domain-specific AI models and frameworks purpose-built for biology, chemistry and scientific research, from protein structure prediction and molecular generation to dynamics simulations and genomic analysis. Packaged as NVIDIA Inference Microservices, or NIM, and deployed as agentic skills, BioNeMo is now designed to integrate into multi-step agentic workflows.

BioNeMo & Snowflake: Combining biological domain intelligence with governed agent orchestration 

Together, Snowflake and NVIDIA make it possible to connect scientific AI capabilities with the governed data, context and orchestration required to operationalize them across the life sciences organization. 

Consider the unlocked agentic scientific discovery workflow: 

A scientist types into Snowflake CoWork: "Generate novel inhibitors for our KRAS G12C program starting from compound X-4217 in our internal library."

With NVIDIA BioNeMo Agent Toolkit integrated into Snowflake’s Cortex Agents (CoCo and CoWork), the Scientific Discovery Agent is armed with skills to execute the following tasks to orchestrate an entire drug discovery pipeline conversationally:

• Retrieve the target's structure and the compound's SMILES using Cortex Analyst

• Generate novel compound candidates with GenMol (de novo) or MolMIM (from a known hit such as X-4217)

• Filter through ADMET assessment with KERMT (fine-tuned with Cortex Training) to eliminate unsafe molecules early

• Dock screened candidates against the protein target with DiffDock

• Score binding affinity with Boltz-2 to rank by predicted potency

Meanwhile, Snowflake's platform capabilities make this production-grade:

• Cortex Analyst provides natural language data retrieval from internal structure-activity-relationship (SAR) databases 

• Dynamic Tables auto-refresh candidate rankings as new experimental data arrives

• Data Sharing delivers hit lists to synthesis CROs in real time without data export

• Data Clean Rooms enable cross-institutional scientific discovery collaboration without exposing IP

• Cortex Training  fine-tunes models like KERMT on proprietary pre-clinical assay data, keeping IP within the perimeter

• All digital assets, including novel generated in-silico candidates, are cataloged under Snowflake Horizon following FAIR principles

The result is an agentic system where specialized biological intelligence acts on trusted enterprise data securely, transparently and at scale. Every prediction is auditable, every result is governed, and every cycle of predict-synthesize-measure-retrain makes the next hypothesis more informed, transforming AI drug discovery from isolated notebook experiments into composable, compliant and continuously-improving organizational capabilities. 

With this collaboration, life sciences organizations can envision what governed agentic AI could make possible, including:

• An AI agent that can reason end-to-end. It can retrieve literature and experimental data, generate candidate molecules, evaluate ADMET properties and surface the most promising leads, all within a governed environment where institutional knowledge informs every decision.

• Multi-step discovery pipelines can become composable, auditable and compliant. BioNeMo handles computational biology, while Snowflake provides context, retrieval, governance, orchestration and lineage tracking.

• Research can compound on itself. Experimental results flow back into the system, and agentic systems refine hypotheses over time. Every observation makes the next hypothesis more informed.

• Agentic clinical operations can support workflows across trial design, patient matching, site selection, patient stratification and real-world evidence generation.

This collaboration enables life sciences organizations to run complex, multi-step scientific workflows directly where their data resides, accelerating novel discovery and reducing molecular development cycle times. By combining NVIDIA’s purpose-built biological domain intelligence with Snowflake’s enterprise-grade security and governance, life sciences companies can streamline and accelerate R&D workflows while helping maintain compliance for sensitive scientific information.
 

The benefits of Snowflake’s and NVIDIA’s shared vision for life sciences R&D include:

• Compressed discovery cycles: AI agents can explore vast molecular and biological spaces in-silico and help focus human expertise where it matters most.

• Shortened development timelines: Agentic workflows can support lead optimization and molecular development, multi-property and bio-signature scoring, and informed pre-clinical and clinical testing.

• More efficient clinical operations: Agentic workflows can support complex processes, from trial design optimization to regulatory submissions, with built-in governance.

• Continuous learning: As every experiment, whether in silico or at the bench, feeds back into the system, organizations can create a continuous learning loop that improves the quality of future hypotheses.

The foundation: an agentic control plane for life sciences 

To deliver on these outcomes, life sciences organizations need more than individual AI models or isolated applications. They need an agentic control plane that brings together compute, governed enterprise data, frontier reasoning and domain-specific scientific intelligence in a single secure operating environment. This is where Snowflake and the BioNeMo Agent Toolkit come together to provide the foundation for enterprise-ready agentic AI in life sciences.
 

The foundation for an agentic control plane in life sciences includes four levels of capabilities working together:

• Infrastructure provides the scalable compute foundation for enterprise AI in life sciences. With NVIDIA Blackwell-class GPU acceleration within Snowflake’s governed enterprise environment, organizations can run demanding training and inference workloads for large-scale biomedical AI use cases.

• Governed data and context ensure AI agents can operate on trusted enterprise knowledge. Proprietary research, genomic, molecular, assay and clinical data can be unified into multimodal data products with consistent, secure centralized governance and semantic context, making them seamlessly accessible without movement or duplication.

• Frontier intelligence provides agents the reasoning and planning needed to decompose complex scientific questions into executable workflows, regardless of whether those capabilities are invoked through APIs, code, agents or data pipelines.

• Domain intelligence brings specialized biological knowledge into the agentic workflow. The BioNeMo Agent Toolkit encodes biological capabilities such as protein folding, molecular dynamics, genomic analysis and biomedical reasoning as agentic skills that can be harnessed as integrated skills in Snowflake CoCo.

When these capabilities converge on a governed platform, they become a powerful mechanism for delivering business outcomes. Life sciences organizations can begin to reshape how they operate across discovery, development, clinical operations and enterprise knowledge management.

The future: Leading agentic innovation in R&D 

As the collaboration grows, the opportunity for Snowflake expands from orchestrating individual scientific workflows to building an enterprise-wide system for governed biomedical intelligence. In this future, researchers, data teams and clinical operators can use natural language to access specialized BioNeMo capabilities, fine-tune models on proprietary data and operationalize agentic workflows directly within Snowflake’s governed environment.

Our shared vision for the future includes:

• Unified scientific workflows: Integrating BioNeMo NIM microservices with Cortex Agents to span molecular simulation, deep multi-omics analysis, clinical retrieval, real-world data analysis, and operational decision-making.

• Secure model fine-tuning: Leveraging Cortex Training to transform general biomedical AI into proprietary scientific intelligence using private data, all without data leaving the Snowflake platform.

• Conversational science: Providing natural-language access to complex BioNeMo workflows, allowing researchers to invoke in-silico screening and scientific discovery through conversation with full institutional governance.

Building the future, together

The future of agentic AI in life sciences is not about any single model or platform. It is about integration: advanced biological AI operating on governed data, orchestrated by agents that understand science, help support compliance and accelerate discovery. The outcome is not only faster workflows but also the potential for greater patient impact.

At Snowflake, we are committed to providing the agentic control plane for life sciences securely, at scale and with governance built in. With the NVIDIA BioNeMo Agent Toolkit bringing purpose-built domain intelligence to enterprise-grade workflow orchestration, Snowflake and NVIDIA are helping life sciences organizations move toward a new model for governed, agentic scientific innovation.

Ready to dive deeper into these topics? Explore our latest resources below:

• Watch the Snowflake Summit Encore: Life Sciences

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• Download: Deploying AI Agents at Scale

  Discover how to move beyond agentic proof of concepts to scalable use cases with our latest ebook: Deploying AI Agents at Scale: 3 Patterns to Move Beyond POCs.

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Forward‑Looking Statements

This blog contains express and implied forward-looking statements, including statements regarding (i) Snowflake’s business strategy, plans, opportunities, or priorities (ii) Snowflake’s products, services, and technology offerings, including those that are under development or not generally available, (iii) market growth, trends, and competitive considerations, (iv) Snowflake’s vision, strategy, and expected benefits relating to artificial intelligence and other emerging product areas, and (v) the integration, interoperability, and availability of Snowflake’s products, services, and technology offerings with and on third-party platforms. These forward-looking statements are subject to a number of risks, uncertainties and assumptions, including those described under the heading “Risk Factors” and elsewhere in the Quarterly Reports on Form 10-Q and the Annual Reports on Form 10-K that Snowflake files with the Securities and Exchange Commission. In light of these risks, uncertainties, and assumptions, actual results could differ materially and adversely from those anticipated or implied in the forward-looking statements. As a result, you should not rely on any forward-looking statements as predictions of future events.