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Posts (6)
- Irreversible Drugs, Real Control: Design for Durable Target Engagement
Over 50% Discount at Checkout Get Yearly Access On-Demand Now — Free 7 Day Trial ➤ DrGPCR Podcast: Jens Carlsson on Predictive Modeling Prediction over explanation—that’s the shift. Jens Carlsson shares how structure-based design, molecular dynamics, and smart collaboration turn models
- Assay Volume Control: Your GPCR Drug Discovery Power Lever
Jens Carlsson (Uppsala University) joins Yamina to explore how computational modeling is evolving from
- AlphaFold’s Breakthrough in GPCR Research: Revolutionizing Discovery, Yet Awaiting Experimental Proof
relies on using the known structure of a homologous protein as a template to model the target protein ( Carlsson References: Carlsson, J. et al.
Other Pages (11)
- ep 175 with jens carlsson clip 3 | Dr. GPCR Ecosystem
Jens Carlsson challenges scientists to go beyond explaining data — and start predicting the future of Home → Flash News → ep 175 with jens carlsson clip 3 AI is changing how we think about structure, function GPCR Podcast AI is changing how we think about structure, function, and discovery — but Jens Carlsson In this clip, Jens unpacks a mindset shift that every computational scientist should hear. Yet Jens’s caution stands: prediction must be proven.
- ep 175 with jens carlsson clip 2 | Dr. GPCR Ecosystem
At Uppsala University, Jens Carlsson’s lab uses modeling to shape experiments—not just explain them. Home → Flash News → ep 175 with jens carlsson clip 2 If your model can’t change an experiment, what’s Jens Carlsson sets in his lab at Uppsala University. Carlsson’s lab doesn’t work in isolation. It’s the catalyst for turning insight into innovation. 🎧 Learn how Carlsson turns models into translational
- ep 175 with jens carlsson clip 1 | Dr. GPCR Ecosystem
Jens Carlsson’s lab at Uppsala University is redefining GPCR drug discovery with predictive molecular Home → Flash News → ep 175 with jens carlsson clip 1 Can your model actually predict the outcome of a Jens Carlsson and his team are redefining what computational modeling means in drug discovery. But Carlsson’s lab stands out for another reason: knowing when not to predict.