agentic control plane<\/a>.<\/p>\nWe are excited to share Snowflake and NVIDIA\u2019s joint vision for bringing agentic AI to life sciences enterprises through a fully governed agentic control plane for R&D.<\/p>\n
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.<\/p>\n
BioNeMo & Snowflake: Combining biological domain intelligence with governed agent orchestration\u00a0<\/p>\n
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.\u00a0<\/p>\n
Consider the unlocked agentic scientific discovery workflow:\u00a0<\/p>\n
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\u2019s 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:<\/p>\n
Retrieve the target’s structure and the compound’s SMILES using Cortex Analyst<\/p>\n
Generate novel compound candidates with GenMol (de novo) or MolMIM (from a known hit such as X-4217)<\/p>\n
Filter through ADMET assessment with KERMT (fine-tuned with Cortex Training) to eliminate unsafe molecules early<\/p>\n
Dock screened candidates against the protein target with DiffDock<\/p>\n
Score binding affinity with Boltz-2 to rank by predicted potency<\/p>\n
Meanwhile, Snowflake’s platform capabilities make this production-grade:<\/p>\n
Cortex Analyst provides natural language data retrieval from internal structure-activity-relationship (SAR) databases\u00a0<\/p>\n
Dynamic Tables auto-refresh candidate rankings as new experimental data arrives<\/p>\n
Data Sharing delivers hit lists to synthesis CROs in real time without data export<\/p>\n
Data Clean Rooms enable cross-institutional scientific discovery collaboration without exposing IP<\/p>\n
Cortex Training\u00a0 fine-tunes models like KERMT on proprietary pre-clinical assay data, keeping IP within the perimeter<\/p>\n
All digital assets, including novel generated in-silico candidates, are cataloged under Snowflake Horizon following FAIR principles<\/p>\n
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.\u00a0<\/p>\n
With this collaboration, life sciences organizations can envision what governed agentic AI could make possible, including:<\/p>\n
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.<\/p>\n
Multi-step discovery pipelines can become composable, auditable and compliant. BioNeMo handles computational biology, while Snowflake provides context, retrieval, governance, orchestration and lineage tracking.<\/p>\n
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.<\/p>\n
Agentic clinical operations can support workflows across trial design, patient matching, site selection, patient stratification and real-world evidence generation.<\/p>\n
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\u2019s purpose-built biological domain intelligence with Snowflake\u2019s enterprise-grade security and governance, life sciences companies can streamline and accelerate R&D workflows while helping maintain compliance for sensitive scientific information.
\u00a0<\/p>\n","protected":false},"excerpt":{"rendered":"Executive Summary Snowflake and the NVIDIA BioNeMo Agent Toolkit bring together governed agentic workflows and specialized biological intelligence.…\n","protected":false},"author":2,"featured_media":87169,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[6],"tags":[179,45171,7493,405,45172,26706,18227,45173,12600],"class_list":["post-87168","post","type-post","status-publish","format-standard","has-post-thumbnail","category-agentic-ai","tag-agentic-ai","tag-agentic-ai-best-practices","tag-agentic-artificial-intelligence","tag-ai-agents","tag-ai-agents-in-life-sciences","tag-ai-data-security","tag-ai-governance-framework","tag-data-governance-for-ai","tag-enterprise-ai-agents"],"_links":{"self":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/87168","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/comments?post=87168"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/posts\/87168\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media\/87169"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/media?parent=87168"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/categories?post=87168"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/ai\/wp-json\/wp\/v2\/tags?post=87168"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}