Imagine having a map that shows you thousands of possible routes but doesn't tell you which roads are actually open. You could plan every route on paper, but eventually, you still need to begin your journey.
AI can predict. Biology validates those predictions.
That's where drug discovery is today. AI is helping researchers map possibilities faster. But turning those predictions into biological evidence still requires experimental validation in the lab.
From prediction to validation
By analyzing vast datasets, spotting patterns, and helping prioritize the targets, compounds, and biological questions worth exploring, it can help researchers explore more efficiently.
But prediction is only the beginning. Experimental studies bring those predictions into the real world, revealing what holds up in biology and what doesn't. Those results can then feed back into the next round of exploration, allowing researchers to refine their questions and make better informed decisions.
AI guides researchers toward what to test. Biology shows what happens - and those results can inform what researchers explore next.
Bringing the lab into the loop
This is the idea behind Lab-in-the-Loop.
- AI generates a hypothesis.
- The laboratory tests it.
- The resulting biological data helps refine the model.
- The improved model generates the next hypothesis.
- The cycle begins again.
The goal isn't for AI to replace the wet lab. It's for computational predictions and biological data to continually inform each other.
Turning predictions into evidence .
Making the Lab-in-the-Loop approach work at scale requires the right tools and data at every stage of the cycle.
That's where Revvity can help. Our portfolio brings together AI-powered informatics, automation, reagents, imaging, analytical technologies, and scientific expertise to power connected Lab-in-the-Loop workflows that transform biological data into actionable scientific insight.
From biological models and high-throughput experiments to data capture and analysis, Revvity brings together the capabilities needed to move from prediction to biological validation and back again.
The result is a workflow where every experiment can generate data that informs what comes next.
What it looks like in practice
Imagine an AI model predicts that a particular compound could produce a specific cellular response.
Instead of accepting the prediction, researchers can test it using multiple biological readouts:
- Does the target respond as expected?
- Does the cell respond?
- Does gene expression change?
- Does the phenotype match the prediction?
Each answer adds another piece of evidence. And that evidence can help researchers refine their hypotheses and inform the model’s next predictions.
At scale, this creates a discovery process where biological data continually informs what researchers explore next.
What comes next
AI is expanding what researchers can imagine. The laboratory provides the biological evidence to show what happens in experimental systems.
The future of drug discovery may depend on bringing those two worlds closer together, not in sequence, but in a continuous cycle of prediction, experimentation, analysis, and refinement.
Because the goal isn't simply to predict what might work. It's to learn, faster and with greater confidence, which predictions hold up in biology.
Bridging the gap between prediction and biological reality. Challenge accepted.
For research use only. Not for use in diagnostic procedures.
