Certara has partnered with NVIDIA to integrate the BioNeMo Agent Toolkit into its drug discovery platform, enabling AI agents to autonomously generate insights across the full development lifecycle while keeping scientists in the decision-making loop. The collaboration unifies Certara's biosimulation software, proprietary datasets, and regulatory expertise with AI-first agentic frameworks, marking a significant shift in how life sciences companies can leverage autonomous AI systems for complex scientific work.

What Are AI Agents and How Do They Work in Drug Development?

AI agents are autonomous systems that can reason over data, models, and tools to accomplish complex tasks without constant human intervention. In the context of drug discovery, these agents function like specialized scientific assistants, capable of analyzing vast datasets and running sophisticated simulations. The NVIDIA BioNeMo Agent Toolkit turns any AI agent into what the companies describe as "an autonomous life sciences scientist," providing access to NVIDIA's full life science computational stack.

Within Certara's platform, these agents will reason over scientific models and clinical data to produce insights across multiple critical areas. Rather than replacing scientists, the agents augment their work by handling repetitive analysis and generating preliminary findings that experts can then evaluate and refine.

What Specific Tasks Will These AI Agents Handle?

The specialized AI agents will tackle several key challenges in drug development, each requiring deep domain knowledge and computational power:

• Dosing Optimization: Using systems pharmacology models to determine optimal drug dosing strategies based on patient populations and clinical data.
• Clinical Data Analysis: Interrogating large clinical datasets to identify patterns and insights that inform development decisions.
• Trial Simulation: Simulating patient scenarios and trial outcomes to predict how drugs will perform in real-world conditions.
• ADMET Evaluation: Assessing absorption, distribution, metabolism, excretion, and toxicity properties of drug candidates early in development.
• Regulatory Evidence Assembly: Preparing regulatory-ready evidence packages that meet the stringent requirements of agencies like the FDA.
• Early Discovery Exploration: Exploring early-stage hypotheses to accelerate the initial phases of drug discovery.

This breadth of capability represents a fundamental shift in how agentic AI can be applied to life sciences. Rather than a single narrow tool, the BioNeMo Agent Toolkit provides a framework that can be adapted across the entire drug development continuum.

How Does This Partnership Keep Scientists Central to the Process?

A critical design principle underlying this collaboration is the "scientist-in-the-loop" workflow. Rather than removing human experts from decision-making, the AI agents are positioned to accelerate insight generation while scientists retain full authority over which findings to pursue and how to interpret results. This approach addresses a common concern about autonomous AI systems in regulated industries: the need to maintain human oversight and accountability.

"Agentic AI combined with Certara's world-class scientists, validated models, and data keeps the scientist in the loop while delivering the speed, scale, and reproducibility our clients need to generate integrated evidence for regulators," said Jon Resnick, Chief Executive Officer of Certara.

Jon Resnick, Chief Executive Officer, Certara

The emphasis on reproducibility is particularly important in pharmaceutical development, where regulatory agencies require transparent, auditable evidence for drug approval. By keeping scientists in the decision-making loop, the partnership ensures that AI-generated insights can be validated and explained to regulators.

Why Does This Matter for the Life Sciences Industry?

Certara serves more than 2,600 biopharmaceutical companies, academic institutions, and global regulatory agencies worldwide, making this partnership potentially influential across the industry. The integration of agentic AI into biosimulation workflows addresses a longstanding challenge in drug development: the time and cost required to generate regulatory-grade evidence.

By automating routine analysis and enabling agents to reason over complex datasets simultaneously, the partnership aims to accelerate the entire development timeline. This matters because bringing a new drug to market currently takes over a decade and billions of dollars; any meaningful acceleration could reduce costs and get life-saving medicines to patients faster.

"We believe it will become increasingly possible to computationally simulate human biology in ways that will transform the discovery and development of new medicines," said Chris Bouton, Chief Technology Officer and Chief AI Officer at Certara.

Chris Bouton, Chief Technology Officer and Chief AI Officer, Certara

The collaboration also signals a broader industry trend: major AI infrastructure companies like NVIDIA are building specialized toolkits for domain-specific applications rather than relying solely on general-purpose large language models. The BioNeMo Agent Toolkit is tailored specifically for life sciences, incorporating domain knowledge and regulatory requirements from the outset.

How to Evaluate Agentic AI Frameworks for Your Organization

If you work in life sciences or another regulated industry considering agentic AI adoption, several factors should guide your evaluation:

• Domain Specialization: Assess whether the framework includes pre-built knowledge and models specific to your industry, rather than requiring you to build everything from scratch.
• Scientist-in-the-Loop Design: Verify that the framework maintains human oversight and decision-making authority, particularly for regulated applications where accountability is critical.
• Reproducibility and Auditability: Ensure the system can explain its reasoning and generate auditable records of how conclusions were reached, which regulators increasingly require.
• Integration with Existing Tools: Check whether the framework integrates with your current software stack and data systems, rather than requiring a complete platform replacement.
• Regulatory Compliance: Confirm that the framework is designed with regulatory requirements in mind, not retrofitted after development.

The Certara-NVIDIA partnership demonstrates that successful agentic AI deployment in high-stakes industries requires both technical sophistication and deep domain expertise. The framework must be powerful enough to handle complex reasoning tasks, yet transparent and controllable enough to satisfy regulatory and scientific standards.

As AI agents become more capable and autonomous, the life sciences industry's approach to maintaining scientist oversight while gaining speed and scale offers a model that other regulated industries may follow. The partnership launches at a moment when agentic frameworks are rapidly evolving, and early adopters who can effectively integrate these tools into existing workflows may gain significant competitive advantages in drug development timelines and costs.