Snowflake and NVIDIA Partner on Agentic AI for Life Sciences
Snowflake and NVIDIA have integrated Snowflake's agentic control plane with NVIDIA's BioNeMo Agent Toolkit to enable governed agentic workflows for life sciences R&D. This collaboration allows pharmaceutical companies to accelerate research by running complex AI workflows directly where their data resides, leveraging specialized biological intelligence and enterprise-grade governance. The solution targets R&D teams, aiming to shorten drug development cycles and enhance scientific discovery through trusted, scalable AI agents.
- →Integrated Agentic AI for Drug Discovery Workflows
- →Leveraging Domain-Specific AI Models for Biology and Chemistry
- →Enhanced Governance and Orchestration for AI Agents
- →Accelerated R&D Cycles and Improved Decision-Making
- →Enabling Continuous Learning in AI Drug Discovery
Features (2) ›
- Integrated Agentic AI for Drug Discovery Workflows
Snowflake's agentic control plane is now integrated with NVIDIA's BioNeMo Agent Toolkit, enabling multi-step scientific AI workflows within Snowflake. This allows life sciences organizations to generate novel compound candidates, filter them based on properties, and rank them conversationally.
- Leveraging Domain-Specific AI Models for Biology and Chemistry
The collaboration utilizes NVIDIA's BioNeMo, packaged as Inference Microservices (NIMs), which provides specialized AI models for tasks like protein structure prediction, molecular generation, and dynamics simulations.
Enhancements (3) ›
- Enhanced Governance and Orchestration for AI Agents
Snowflake's platform capabilities, including Cortex Analyst for data retrieval, Dynamic Tables for auto-refreshing rankings, and Data Sharing for CRO collaboration, provide governance, orchestration, and lineage tracking for agentic AI workflows.
- Accelerated R&D Cycles and Improved Decision-Making
The integration aims to compress discovery cycles by enabling AI agents to explore vast molecular and biological spaces, thereby focusing human expertise and shortening molecular development timelines.
- Enabling Continuous Learning in AI Drug Discovery
Experimental results from both in-silico and wet-lab experiments can feed back into the system, creating a continuous learning loop that refines hypotheses and improves the quality of future scientific endeavors.
Notes (1) ›
- Vision for AI in Life Sciences
This partnership underscores a vision for enterprise-ready agentic AI in life sciences, moving beyond single-purpose tools towards adaptive systems that can reason, plan, and execute directly within business-critical workflows.
https://www.snowflake.com/content/snowflake-site/global/en/blog/snowflake-nvidia-bionemo-agentic-ai-life-sciences