Drug discovery teams are running short of new ideas to test, and AI models that simulate how cells respond to drugs only pay off once leaders know when those predictions can be trusted with experiments, timelines, and budget.
In this episode, Daniel Veres, Co-Founder and Chief Scientific Officer at Turbine, and Giorgio Gaglia, Head of Systems and Disease Biology at Sanofi, examine with host Yolandi de Weerdt how testing models on data they have never seen shows which R&D decisions their predictions can reliably inform.
The discussion offers R&D leaders a practical way to judge when a model's predictions are ready to inform which experiments to run, where in the pipeline to start, and how much risk to accept.
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