Equipping scientists with better insights for stronger pipelines
Imagine trying to solve a puzzle with millions of pieces but only seeing a handful at a time. That’s what drug discovery has long felt like.
Scientists generate enormous amounts of data, molecular structures, experimental results, and biological responses, but no single team can see the full picture. The insights are there, scattered across labs, organizations, and years of research.
The challenge isn’t creating data. It’s connecting it in a way that reveals what’s possible next.
When discovery outpaces understanding
The pace of science has never been faster. But with that speed comes complexity. Researchers are expected to design better molecules, predict outcomes earlier, and reduce costly trial-and-error. Yet even with advances in artificial intelligence, one limitation remains:
AI is only as powerful as the data it learns from. Most organizations don’t have enough data on their own to fully unlock that potential. At the same time, sharing data openly isn’t an option.
So, the gap emerges… between the promise of AI and the reality of how discovery actually happens.
A new model for discovery, built together
What if scientists didn’t have to choose between access and control?
Through a new collaboration with Lilly, a new approach is making that possible.
Lilly’s predictive AI models, trained on decades of research, are now being made accessible to the broader biotech community. But access alone isn’t the breakthrough.
Participating organizations contribute their own experimental data to improve these models over time, without ever sharing that data directly. Through a federated learning framework, models learn from many sources while data remains private and secure.
What was once siloed becomes shared intelligence. What was once fragmented becomes connected.
Bringing AI to where science happens
The real shift isn’t just access to AI. It’s how seamlessly it fits into the way scientists already work.
Revvity Signals delivers these models directly into existing workflows, where data is captured, analyzed, and acted upon every day. By embedding AI into discovery workflows, predictive insight becomes part of everyday decision-making, not a separate step. This means:
- No exporting data.
- No switching systems.
- No disruption on how research happens.
Instead, scientists can design, test, and refine molecules with insight drawn from far beyond their own datasets.
From global pharma to emerging biotech, organizations of all sizes can now apply advanced AI directly to their own discovery programs, while maintaining full control of their data.
Turning collective intelligence into real-world impact
As more organizations participate, something powerful happens.
Each contribution strengthens the models.
Each insight improves the next prediction.
Each experiment helps accelerate the next breakthrough.
What begins as individual discovery becomes collective progress.
This is more than a new technology. It’s a new way of advancing science.
One where data stays protected, but knowledge expands. Where collaboration drives progress. Where discovery is no longer limited by the boundaries of a single organization.
The future of drug discovery won’t be built in isolation, because the gap between data and discovery is closing.
Challenge accepted.
