Making “Undruggable” Proteins Druggable? | Dr. Linker & Dr. Schnee, Merck KGaA, Darmstadt, Germany

In this episode of Tech & Drugs, I sit down with Dr. Stephanie Linker and Dr. Philipp Schnee from Merck KGaA, Darmstadt, Germany to explore how AI is changing protein design, drug discovery, and the role of scientists.We discuss de novo protein design, structure prediction, AlphaFold, scientific agents, AI adoption, and what happens when computational models suggest biological solutions that scientists might never have considered themselves.Stephanie works as a Senior Computational Biochemist in the Digital Innovation team at Merck KGaA, Darmstadt, Germany, focusing on AI-driven protein design and structure prediction. Philipp works in the Data & AI environment at Merck KGaA, Darmstadt, Germany, bringing together scientific expertise and technology to develop end-to-end solutions, including a de novo protein design platform.A central theme of our conversation is the move from predicting biology to increasingly being able to engineer it. Stephanie describes proteins as the “machines of the cell”—and explains how de novo design allows scientists to start designing proteins for specific interactions rather than only discovering what already exists in nature.We also discuss what happens when AI challenges established scientific intuition. Stephanie shares an example of applying new protein-design methods to a target that had previously been considered “undruggable”—and how the results challenged those initial assumptions.Beyond protein design, we explore a much bigger change in scientific work. If AI can access information, write code, generate hypotheses and increasingly orchestrate parts of the research process, where does human scientific value move?For Stephanie and Philipp, curiosity, asking the right questions, connecting disciplines, choosing where to focus, and building trust around AI-generated results become increasingly important.Key themes discussed• AI-driven protein structure prediction and de novo protein design• Moving from predicting proteins to engineering new proteins• AlphaFold, co-folding models and protein language models• Exploring targets previously considered “undruggable”• When AI-generated designs challenge scientific intuition• Balancing exploration and exploitation in protein design• Why transparency and trust matter when scientists use AI• Building an internal protein-design platform for scientists• Making advanced AI tools accessible beyond AI specialists• How scientific agents could change research workflows• Why the scientist of the future may increasingly orchestrate AI agents• Bottom-up versus top-down AI adoption• Why AI in science goes far beyond chatbots and LLMs• Open-source models, frontier models and technological dependency• Choosing the right AI model for the right scientific task• Training scientists and the potential role of AI champions• World models and connecting digital models with physical experiments• What AI-enabled science could look like over the next five yearsGuestsDr. Stephanie LinkerSenior Computational BiochemistMerck KGaA, Darmstadt, GermanyLinkedIn: https://www.linkedin.com/in/stephanie-linker-957ba1188/Dr. Philipp SchneeData & AI / Go GlobalMerck KGaA, Darmstadt, GermanyLinkedIn: https://www.linkedin.com/in/philippschnee/