Revvity and Eli Lilly and Company have launched a federated AI drug discovery platform that gives biotech laboratories access to Lilly’s predictive models inside their existing experimental data systems. The collaboration makes Lilly TuneLab available through Revvity’s Signals Xynthetica platform, which the companies describe as a scalable, federated framework designed to accelerate AI-enabled drug discovery.
AI drug discovery platforms use machine learning models trained on large experimental datasets to predict how compounds will behave before they are synthesized or tested in the lab. These systems help teams prioritize compounds, guide medicinal chemistry decisions, and reduce the number of failed experiments needed to identify viable drug candidates. For lab managers, the new platform signals a shift toward embedding AI directly into day-to-day discovery workflows, especially for small and midsized biotechs that lack the resources to build large in-house model libraries.
What the Revvity and Lilly collaboration provides
The collaboration gives biotech organizations access to Lilly’s internal predictive models through Revvity’s cloud-based Signals platform. Lilly TuneLab was created to make advanced AI and machine learning models—trained on decades of Lilly research data—available to the broader biotech community in exchange for data contributions that improve model performance through federated learning.
Unlike traditional AI tools that require organizations to upload large datasets to a central repository, federated learning keeps proprietary data inside each organization. Models move to the data rather than the other way around, allowing participants to apply Lilly’s models to their own discovery programs while maintaining privacy and security.
Kevin Willoe, president of Revvity Signals Software, said, “Federated learning represents one of the most powerful paths forward for AI in drug discovery, but it requires the right platform to succeed.”
How Signals Xynthetica fits into laboratory operations
Signals Xynthetica functions as a models-as-a-service layer within Revvity’s existing Signals environment. That placement allows researchers to run AI predictions on data that has already been captured, curated, and analyzed, rather than exporting datasets to external modeling tools.
The platform builds on Revvity’s broader Signals infrastructure. Signals One supports wet-lab data capture and orchestration across experiments, while Signals Synergy enables secure data exchange with contract research organizations (CROs), academic collaborators, and external partners. Those capabilities provide the foundation for operating a scalable federated learning network that connects multiple organizations without centralizing sensitive data.
By embedding Lilly TuneLab models into this environment, Revvity positions AI predictions alongside experimental design, data management, and collaboration, making the AI drug discovery platform part of routine laboratory operations rather than a separate analytics step.
Why federated learning matters for biotech labs
Small and midsized biotech organizations generate highly diverse and valuable experimental data, but they often lack the scale needed to train robust AI models on their own. Federated learning allows those datasets to enhance shared predictive performance while keeping intellectual property private and secure.
From a laboratory management perspective, this approach changes how AI adoption is funded and governed. Instead of investing in dedicated data science infrastructure, labs can apply Lilly’s pre-trained models to their discovery programs while retaining control of raw data and workflows.
The collaboration also includes joint funding from Revvity and Lilly for selected participants, providing access to Signals One, Signals Xynthetica, and modeling credits. This co-funded model lowers barriers to entry for organizations evaluating AI-driven discovery.
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What lab managers should watch for
For lab managers overseeing discovery operations, the Revvity–Lilly collaboration highlights several trends that will shape how teams work:
- AI is moving into core lab infrastructure, not just external analytics tools
- Data governance and privacy controls are becoming central to AI deployment
- Collaborative AI networks are linking CROs, academic labs, and biotechs through shared model ecosystems
As AI becomes embedded in experimental planning, data capture, and partner collaboration, platforms such as Signals Xynthetica may increasingly influence how labs staff, budget, and structure discovery workflows across the biotech sector.
This article was created with the assistance of Generative AI and has undergone editorial review before publishing.
