Can AI Really Design New Drugs? Google DeepMind Spin-out Isomorphic Labs Explains

Can AI move from predicting proteins to actually designing new drugs? Isomorphic Labs is trying to answer one of the biggest questions in science.In this episode of The Neuron, Corey Noles and Grant Harvey talk with Rebecca Paul, Head of Medicinal Drug Design at Isomorphic Labs, and Michael Schaarschmidt, Foundational AI Research Lead.They explain why drug discovery is so slow, expensive, and failure-prone—and why AI drug design is much more complicated than “generate a molecule and ship it.” The conversation covers AlphaFold, structure prediction, molecule generation, binding models, clinical failure rates, human trust in AI systems, and the long-term hope of designing drugs for targets once considered “undruggable.”In this episode:Why drug discovery can take more than a decadeWhat people misunderstand about “AI-designed drugs”How medicinal chemists actually use AI modelsWhy biology is harder than text, images, or codeWhat it would take to make drug discovery faster and cheaperThe dream of designing a drug candidate in one iterationWhy “undruggable” proteins may not stay undruggable foreverAdditional resources:Technical report blog Best resource for learning about the capabilities that we are buildingIsomorphic Labs websiteBest destination for learning more about Iso and joining our team in London, Lausanne or Cambridge, MASubscribe for more grounded conversations on how AI is changing science, work, and the world.For more practical, grounded conversations on AI systems that actually work, subscribe to The Neuron newsletter at https://theneuron.ai.