If your model can’t change an experiment, what’s the point?

If your model can’t change an experiment, what’s the point?

That’s the standard Dr. Jens Carlsson sets in his lab at Uppsala University. For him, modeling isn’t just about elegant simulations; it’s about impact. The kind of impact that shows up in how experiments are designed, which compounds get prioritized, and what gets synthesized next.

Carlsson’s lab doesn’t work in isolation. They collaborate deeply with medicinal chemists, pharmacologists, and biotech partners to create workflows that connect virtual screening to synthesis and bioassay. Every step has a purpose. Every prediction feeds into a testable hypothesis.

But the real differentiator? The way they collaborate: strategically, transparently, and without ego. His team is clear about the capabilities and limitations of their models, an honesty that builds long-term trust across disciplines.

In GPCR drug discovery, where complexity is the rule and timelines are tight, this kind of cross-functional fluency is no longer optional. It’s the catalyst for turning insight into innovation.

🎧 Learn how Carlsson turns models into translational outcomes in this episode of the Dr. GPCR Podcast: model predict discover 

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