Send a text Over the past few years, artificial intelligence has rapidly entered drug discovery — but one of the true “holy grail” challenges inside pharma is no longer just predicting what proteins look like, but understanding how molecules actually interact: how proteins bind drugs, antibodies, RNA, and each other, and how those insights can guide better decisions long before anything reaches the lab. Early breakthroughs in structure prediction made protein models widely accessible, but real biology happens at interfaces, in motion, and often in fleeting conformations that determine whether a therapy ultimately succeeds or fails. Today’s conversation explores what it means to move into this next chapter — where structural predictions are translated into actionable insight for real-world drug development. Joining us are two scientists from Merck KGaA, Darmstadt, Germany ( https://www.emdgroup.com/en ), working at the intersection of protein structure prediction, molecular dynamics, and generative design, helping to build internal platforms that turn computational models into practical decision tools for therapeutic discovery. Dr. Stephanie Linker, Ph.D. is a Senior Computational Biochemist in Merck’s Group Digital Innovation unit, where she leads initiatives in generative antibody design, de novo protein binder development, and advanced structure prediction platforms. Her work focuses on how molecular shape, flexibility, and dynamics influence whether a designed molecule actually performs in biological systems. Dr. Philipp Schnee, Ph.D. is a Computational Protein Design expert at Merck KGaA, currently part of the GoGlobal Data & AI rotation program. His research bridges high-resolution molecular dynamics simulations with experimental biochemistry to understand protein function, mutation effects, and mechanisms that can be leveraged for enzyme engineering and inhibitor design. Together, their work reflects a broader shift happening across the pharmaceutical industry — away from static structures and standalone models, and toward integrated platforms that combine folding, binding, ranking, and experimental validation to guide smarter, faster therapeutic decisions. In this episode, we explore what these next-generation tools can do today, where their limitations remain, and why the ability to move from structure prediction to decision-ready insight may become one of the most important frontiers in modern drug discovery. AI drug discovery, protein structure prediction, computational biology, biologics design, pharmaceutical R&D #DrugDiscovery #ArtificialIntelligence #AlphaFold #ProteinFolding #Biotech #PharmaInnovation #ComputationalBiology #StructuralBiology #AIinHealthcare #AntibodyEngineering #MolecularDynamics #FutureOfMedicine #SystemsBiology #LifeSciences #ProgressPotentialPossibilities #MachineLearning #BioTechPodcast Support the show
